Automated robotic process selection and configuration

ABSTRACT

A system for selection and configuration of an automated robotic process includes a media input module structured to receive at least one functional media, a media analysis module structured to analyze the at least one functional media and identify an action parameter; and a solution selection module structured to select at least one component of an AI solution for use in an automated robotic process, wherein the selection is based, at least in part, on the action parameter.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of priority of and is acontinuation of PCT Application PCT/US2021/016473 (SFTX-0013-WO), filedFeb. 3, 2021, entitled “ARTIFICIAL INTELLIGENCE SELECTION ANDCONFIGURATION.”

PCT Application PCT/US2021/016473 (SFTX-0013-WO) claims the benefit ofpriority of and is a continuation-in-part of U.S. patent applicationSer. No. 16/780,519 (Attorney Docket No. SFTX-0012-U01), filed Feb. 3,2020, entitled “ADAPTIVE INTELLIGENCE AND SHARED INFRASTRUCTURE LENDINGTRANSACTION ENABLEMENT PLATFORM RESPONSIVE TO CROWD SOURCEDINFORMATION.”

PCT Application PCT/US2021/016473 (SFTX-0013-WO) also claims priority tothe following U.S. Provisional Patent Applications: Ser. No. 63/127,980(Attorney Docket No. SFTX-0016-P01), filed Dec. 18, 2020, entitled“MARKET ORCHESTRATION SYSTEM FOR FACILITATING ELECTRONIC MARKETPLACETRANSACTIONS”; Ser. No. 63/069,542 (Attorney Docket No. SFTX-0015-P01),filed Aug. 24, 2020, entitled “INFORMATION TECHNOLOGY SYSTEMS ANDMETHODS FOR TRANSACTION ARTIFICIAL INTELLIGENCE LEVERAGING DIGITALTWINS”; and Ser. No. 62/994,581 (Attorney Docket No. SFTX-0014-P01),filed Mar. 25, 2020, entitled “COMPLIANCE SYSTEM FOR FACILITATINGLICENSING OF PERSONALITY RIGHTS”.

Each of the foregoing applications is incorporated herein by referencein its entirety.

BACKGROUND Field

This application is related to the field of lending, and moreparticularly to the field of adaptive intelligent systems used to enablelending transactions.

Description of the Related Art

Lending transactions provide financing for a wide variety of needs,ranging from housing and education to corporate and government projects,among many others, while enabling lenders to earn financial returns.However, lending transactions are plagued by a number of problems,including opacity and asymmetry of information, moral hazard induced byshifting of the consequences of risky or inappropriate behavior,complexity of application and negotiation processes, burdensomeregulatory and policy regimes, difficulty in determining the value ofproperty that is used as collateral or backing for obligations,difficulty in determining the reliability or financial health ofentities, and others. A need exists for lending systems that addressthese and other problems of lending transactions and environments.

SUMMARY

Provided herein is a lending transaction enablement platform having aset of data-integrated microservices including data collection andmonitoring services, blockchain services, and smart contract servicesfor handling lending entities and transactions. The platform is capableof enabling a wide range of dedicated solutions, which may share datacollection and storage infrastructure, and which may share or exchangeinputs, events, activities, and outputs, such as to reinforce learning,enable automation, and enable adaptive intelligence across the varioussolutions.

In embodiments a lending platform is provided having an Internet ofThings and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debttransaction.

In embodiments a lending platform is provided having a smart contractand distributed ledger platform for managing at least one of ownershipof a set of collateral and a set of events related to a set ofcollateral.

In embodiments a lending platform is provided having a smart contractsystem that automatically adjusts an interest rate for a loan based oninformation collected via at least one of an Internet of Things system,a crowdsourcing system, a set of social network analytic services and aset of data collection and monitoring services.

In embodiments a lending platform is provided having a crowdsourcingsystem for obtaining information about at least one of a state of a setof collateral for a loan and a state of an entity relevant to aguarantee for a loan.

In embodiments a lending platform is provided having a smart contractthat automatically adjusts an interest rate for a loan based on at leastone of a regulatory factor and a market factor for a specificjurisdiction.

In embodiments a lending platform is provided having a smart contractthat automatically restructures debt based on a monitored condition.

In embodiments a lending platform is provided having a social networkmonitoring system for validating the reliability of a guarantee for aloan.

In embodiments a lending platform is provided having an Internet ofThings data collection and monitoring system for validating reliabilityof a guarantee for a loan.

In embodiments a lending platform is provided having a robotic processautomation system for negotiation of a set of terms and conditions for aloan.

In embodiments a lending platform is provided having a robotic processautomation system for loan collection.

In embodiments a lending platform is provided having a robotic processautomation system for consolidating a set of loans.

In embodiments a lending platform is provided having a robotic processautomation system for managing a factoring loan.

In embodiments a lending platform is provided having a robotic processautomation system for brokering a mortgage loan.

In embodiments a lending platform is provided having a crowdsourcing andautomated classification system for validating condition of an issuerfor a bond.

In embodiments a lending platform is provided having a social networkmonitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided having an Internet ofThings data collection and monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments a lending platform is provided having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored by the IoT.

In embodiments a lending platform is provided having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored in a social network.

In embodiments a lending platform is provided having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing.

In embodiments a lending platform is provided having an automatedblockchain custody service for managing a set of custodial assets.

In embodiments a lending platform is provided having an underwritingsystem for a loan with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions.

In embodiments a lending platform is provided having a loan marketingsystem with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services and smart contract services for marketing a loanto a set of prospective parties.

In embodiments a lending platform is provided having a rating systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities.

In embodiments a lending platform is provided having a compliance systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation and a policy thatapplies to a lending transaction.

One aspect of the present disclosure relates to a method forelectronically facilitating licensing of one or more personality rightsof a licensor. The method may include receiving an access request from alicensee to obtain approval to license personality rights from a set ofavailable licensors. The method may include selectively granting accessto the licensee based on the access request. The method may includereceiving confirmation of a deposit of an amount of funds from thelicensee. The method may include issuing an amount of cryptocurrencycorresponding to the amount of funds deposited by the licensee to anaccount of the licensee. The method may include receiving a smartcontract request to create a smart contract governing the licensing ofthe one or more personality rights of the licensor by the licensee. Thesmart contract request may indicate one or more terms including aconsideration amount of cryptocurrency to be paid to the licensor inexchange for one or more obligations on the licensor. The method mayinclude generating the smart contract based on the smart contractrequest. The method may include escrowing the consideration amount ofcryptocurrency from the account of the licensee. The method may includedeploying the smart contract to a distributed ledger. The method mayinclude verifying, by the smart contract, that the licensor hasperformed the one or more obligations. The method may include, inresponse to receiving verification that the licensor has performed theone or more obligations, releasing at least a portion of theconsideration amount of cryptocurrency into a licensor account of thelicensor. The method may include outputting a record indicating acompletion of a licensing transaction defined by the smart contract tothe distributed ledger.

In some implementations of the method, the smart contract may begenerated using a smart contract template provided by an interestedthird party.

In some implementations of the method, the interested third party may beone of a university, a sports team, or a collegiate athletics governanceorganization.

In some implementations of the method, the distributed ledger may beauditable by a set of third parties, including the interested thirdparty.

In some implementations of the method, the cryptocurrency may be one ofBitcoin, Ethereum, Litecoin, and Ripple.

In some implementations of the method, the cryptocurrency may be aprivate cryptocurrency.

In some implementations of the method, the cryptocurrency may be peggedto a particular type of real currency.

In some implementations of the method, the distributed ledger may be apublic ledger.

In some implementations of the method, the distributed ledger may be aprivate ledger that is only hosted on computing devices associated withinterested third parties.

In some implementations of the method, the distributed ledger may be ablockchain.

In some implementations of the method, verifying that the licensor mayhave performed the one or more obligations includes receiving locationdata from a wearable device associated with the licensor. In someimplementations of the method, verifying that the licensor may haveperformed the one or more obligations includes verifying that thelicensor has performed the one or more obligations based on the locationdata.

In some implementations of the method, verifying that the licensor mayhave performed the one or more obligations includes receiving socialmedia data from a social media website. In some implementations of themethod, verifying that the licensor may have performed the one or moreobligations includes verifying that the licensor has performed the oneor more obligations based on the social media data.

In some implementations of the method, verifying that the licensor mayhave performed the one or more obligations includes receiving mediacontent from an external data source. In some implementations of themethod, verifying that the licensor may have performed the one or moreobligations includes verifying that the licensor has performed the oneor more obligations based on the media content.

In some implementations of the method, the media content may be one of avideo recording, a photograph, or an audio recording.

In some implementations of the method, selectively granting access tothe licensor may include receiving a set of affiliations of thelicensee. In some implementations of the method, selectively grantingaccess to the licensor may include verifying that the licensee ispermitted to engage with a set of licensors including the licensor basedon the set of affiliations. In some implementations of the method,selectively granting access to the licensor may include in response toverifying that the licensee is permitted to engage with the set oflicensors, granting the licensee approval to engage with the set oflicensees.

In some implementations of the method, the set of affiliations of thelicensee may include organizations to which the licensee or a principalassociated with the licensee donates to or owns.

In some implementations of the method, releasing at least a portion ofthe consideration amount of cryptocurrency into a licensee account ofthe licensee may include identifying an allocation smart contractassociated with the licensee. In some implementations of the method, theallocation smart contract may define allocation rules governing a mannerby which funds resulting from licensing the one or more personalityrights are to be distributed amongst the licensor and one or moreadditional entities. In some implementations of the method, releasing atleast a portion of the consideration amount of cryptocurrency into alicensee account of the licensee may include distributing theconsideration amount of the cryptocurrency in accordance with theallocation rules.

In some implementations of the method, the additional entities mayinclude one or more of teammates of the licensor, coaches of thelicensor, a team of the licensor, a university of the licensee, and theNCAA.

In some implementations of the method, it may include obtaining a set ofrecords indicating completion of a set of respective transactions fromthe distributed ledger. In some implementations of the method, the setof records may include the record indicating the completion of thetransaction defined by the smart contract. In some implementations ofthe method, it may include determining whether an organizationassociated with the licensor is likely in violation of one or moreregulations based on the set of records and a fraud detection model.

In some implementations of the method, the fraud detection model may betrained using training data that indicates permissible transactions andfraudulent transactions.

Another aspect of the present disclosure relates to a system configuredfor electronically facilitating licensing of one or more personalityrights of a licensor. The system may include one or more hardwareprocessors configured by machine-readable instructions. The processor(s)may be configured to receive an access request from a licensee to obtainapproval to license personality rights from a set of availablelicensors. The processor(s) may be configured to selectively grantaccess to the licensee based on the access request. The processor(s) maybe configured to receive confirmation of a deposit of an amount of fundsfrom the licensee. The processor(s) may be configured to issue an amountof cryptocurrency corresponding to the amount of funds deposited by thelicensee to an account of the licensee. The processor(s) may beconfigured to receive a smart contract request to create a smartcontract governing the licensing of the one or more personality rightsof the licensor by the licensee. The smart contract request may indicateone or more terms including a consideration amount of cryptocurrency tobe paid to the licensor in exchange for one or more obligations on thelicensor. The processor(s) may be configured to generate the smartcontract based on the smart contract request. The processor(s) may beconfigured to escrow the consideration amount of cryptocurrency from theaccount of the licensee. The processor(s) may be configured to deploythe smart contract to a distributed ledger. The processor(s) may beconfigured to verify, by the smart contract, that the licensor hasperformed the one or more obligations. The processor(s) may beconfigured to, in response to receiving verification that the licensorhas performed the one or more obligations, release at least a portion ofthe consideration amount of cryptocurrency into a licensor account ofthe licensor. The processor(s) may be configured to output a recordindicating a completion of a licensing transaction defined by the smartcontract to the distributed ledger.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts components and interactions of an embodiment of a lendingplatform having a set of data-integrated microservices including datacollection and monitoring services for handling lending entities andtransactions.

FIG. 2 depicts components and interactions of an embodiment of a lendingplatform in which a set of lending solutions are supported by adata-integrated set of data collection and monitoring services, adaptiveintelligent systems, and data storage systems.

FIG. 3 depicts components and interactions of an embodiment of a lendingplatform having a set of data integrated blockchain services, smartcontract services, social network analytic services, crowdsourcingservices and Internet of Things data collection and monitoring servicesfor collecting, monitoring, and processing information about entitiesinvolved in or related to a lending transaction.

FIG. 4 depicts components and interactions of a lending platform havingan Internet of Things and sensor platform for monitoring at least one ofa set of assets, a set of collateral, and a guarantee for a loan, abond, or a debt transaction.

FIG. 5 depicts components and interactions of a lending platform havinga crowdsourcing system for collecting information related to entitiesinvolved in a lending transaction.

FIG. 6 depicts an embodiment of a crowdsourcing workflow enabled by alending platform.

FIG. 7 depicts components and interactions of an embodiment of a lendingplatform having a smart contract system that automatically adjusts aninterest rate for a loan based on information collected via at least oneof an Internet of Things system, a crowdsourcing system, a set of socialnetwork analytic services and a set of data collection and monitoringservices.

FIG. 8 depicts components and interactions of an embodiment of a lendingplatform having a having a smart contract that automaticallyrestructures debt based on a monitored condition.

FIG. 9 depicts components and interactions of a lending platform havinga set of data collection and monitoring systems for validating thereliability of a guarantee for a loan, including an Internet of Thingssystem and a social network analytics system.

FIG. 10 depicts components and interactions of a lending platform havinga robotic process automation system for negotiation of a set of termsand conditions for a loan.

FIG. 11 depicts components and interactions of a lending platform havinga robotic process automation system for loan collection.

FIG. 12 depicts components and interactions of a lending platform havinga robotic process automation system for consolidating a set of loans.

FIG. 13 depicts components and interactions of a lending platform havinga robotic process automation system for managing a factoring loan.

FIG. 14 depicts components and interactions of a lending platform havinga robotic process automation system for brokering a mortgage loan.

FIG. 15 depicts components and interactions of a lending platform havinga crowdsourcing and automated classification system for validatingcondition of an issuer for a bond, a social network monitoring systemwith artificial intelligence for classifying a condition about a bond,and an Internet of Things data collection and monitoring system withartificial intelligence for classifying a condition about a bond.

FIG. 16 depicts components and interactions of a lending platform havinga system that manages the terms and conditions of a loan based on aparameter monitored by the IoT, by a parameter determined by a socialnetwork analytic system, or a parameter determined by a crowdsourcingsystem.

FIG. 17 depicts components and interactions of a lending platform havingan automated blockchain custody service for managing a set of custodialassets.

FIG. 18 depicts components and interactions of a lending platform havingan underwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.

FIG. 19 depicts components and interactions of a lending platform havinga loan marketing system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services and smart contract services formarketing a loan to a set of prospective parties.

FIG. 20 depicts components and interactions of a lending platform havinga rating system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities.

FIG. 21 depicts components and interactions of a lending platform havinga regulatory and/or compliance system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for automatically facilitating compliance with atleast one of a law, a regulation and a policy that applies to a lendingtransaction.

FIG. 22 to FIG. 49 are schematic diagrams of embodiments of neural netsystems that may connect to, be integrated in, and be accessible by theplatform for enabling intelligent lending and transactions includingones involving expert systems, self-organization, machine learning,artificial intelligence and including neural net systems trained forpattern recognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes in accordance with embodiments of the present disclosure.

FIG. 50 depicts general components and interactions of a lendingplatform.

FIG. 51 depicts components and interactions of a lending platform thatleverages entity data to identify loan-events and initiate automaticloan-actions.

FIG. 52 depicts a method of processing entity data to initiate automaticloan-actions.

FIG. 53 depicts components and interactions of a lending platform tovalue collateral and determine collateral condition.

FIG. 54 depicts a method of processing collateral data to determine acollateral condition and initiate loan-actions in response.

FIG. 55 depicts components and interactions of a lending platform.

FIG. 56 depicts a method of a lending platform.

FIG. 57 depicts components and interactions of a lending platform thatidentifies a collateral event and initiates an automatic action inresponse.

FIG. 58 depicts a method of a lending platform that automaticallyinitiates a loan-action in response to a collateral event.

FIG. 59 depicts components and interactions of a lending platform.

FIG. 60 depicts a method of a lending platform.

FIG. 61 depicts components and interactions of a lending platform.

FIG. 62 depicts a method of a lending platform.

FIG. 63 depicts components and interactions of a lending platform.

FIG. 64 depicts a method of a lending platform.

FIG. 65 depicts components and interactions of a lending platform.

FIG. 66 depicts a method of a lending platform.

FIG. 67 depicts components and interactions of a lending platform.

FIG. 68 depicts a method of a lending platform.

FIG. 69 depicts components and interactions of a lending platform.

FIG. 70 depicts a method of a lending platform.

FIG. 71 depicts components and interactions of a lending platform.

FIG. 72 depicts a method of a lending platform.

FIG. 73 depicts components and interactions of a lending platform.

FIG. 74 depicts a method of a lending platform.

FIG. 75 depicts components and interactions of a lending platform.

FIG. 76 depicts a method of a lending platform.

FIG. 77 depicts components and interactions of a lending platform.

FIG. 78 depicts a method of a lending platform.

FIG. 79 depicts components and interactions of a lending platform.

FIG. 80 depicts a method of a lending platform.

FIG. 81 depicts components and interactions of a lending platform.

FIG. 82 depicts a method of a lending platform.

FIG. 83 depicts components and interactions of a lending platform.

FIG. 84 depicts a method of a lending platform.

FIG. 85 depicts components and interactions of a lending platform.

FIG. 86 depicts a method of a lending platform.

FIG. 87 depicts components and interactions of a lending platform.

FIG. 88 depicts a method of a lending platform.

FIG. 89 depicts components and interactions of a lending platform.

FIG. 90 depicts a method of a lending platform.

FIG. 91 depicts components and interactions of a lending platform.

FIG. 92 depicts a method of a lending platform.

FIG. 93 depicts components and interactions of a lending platform.

FIG. 94 depicts a method of a lending platform.

FIG. 95 depicts components and interactions of a lending platform.

FIG. 96 depicts a method of a lending platform.

FIG. 97 depicts components and interactions of a lending platform.

FIG. 98 depicts a method of a lending platform.

FIG. 99 depicts components and interactions of a lending platform.

FIG. 100 depicts a method of a lending platform.

FIG. 101 depicts components and interactions of a lending platform.

FIG. 102 depicts a method of a lending platform.

FIG. 103 depicts components and interactions of a lending platform.

FIG. 104 depicts a method of a lending platform.

FIG. 105 depicts components and interactions of a lending platform.

FIG. 106 depicts a method of a lending platform.

FIG. 107 depicts components and interactions of a lending platform.

FIG. 108 depicts a method of a lending platform.

FIG. 109 depicts components and interactions of a lending platform.

FIG. 110 depicts a method of a lending platform.

FIG. 111 depicts a schematic illustrating an example of a portion of aninformation technology system for transaction artificial intelligenceleveraging digital twins according to some embodiments of the presentdisclosure.

FIG. 112 depicts a schematic illustrating a compliance system thatfacilitates the licensing of personality rights according to someembodiments of the present disclosure.

FIG. 113 depicts a schematic illustrating an example set of componentsof a compliance system according to some embodiments of the presentdisclosure.

FIG. 114 depicts a set of operations of a method for vetting a potentiallicensee for purposes of licensing personality rights of a licensoraccording to some embodiments of the present disclosure.

FIG. 115 depicts a set of operations of a method for facilitating thelicensing of personality rights of a licensor by a licensee according tosome embodiments of the present disclosure.

FIG. 116 depicts a set of operations of a method for detecting potentialcircumvention of rules or regulations by a licensor and/or licenseeaccording to some embodiments of the present disclosure.

FIG. 117 depicts a method for selecting an AI solution.

FIG. 118 depicts a method for selecting an AI solution.

FIG. 119 depicts an example of an assembled AI solution.

FIG. 120 depicts a method for selecting an AI solution.

FIG. 121 depicts a method for selecting an AI solution.

FIG. 122 depicts an AI solution selection and configuration system.

FIG. 123 depicts an AI solution selection and configuration system.

FIG. 124 depicts an AI solution selection and configuration system.

FIG. 125 depicts a component configuration circuit.

FIG. 126 depicts an AI solution selection and configuration system.

FIG. 127 depicts a system for selecting and configuring an artificialintelligence model.

FIG. 128 depicts a method of selecting and configuring an artificialintelligence model.

DETAILED DESCRIPTION

The term services/microservices (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a service/microservice includesany system (or platform) configured to functionally perform theoperations of the service, where the system may be data-integrated,including data collection circuits, blockchain circuits, artificialintelligence circuits, and/or smart contract circuits for handlinglending entities and transactions. Services/microservices may facilitatedata handling and may include facilities for data extraction,transformation and loading; data cleansing and deduplication facilities;data normalization facilities; data synchronization facilities; datasecurity facilities; computational facilities (e.g., for performingpre-defined calculation operations on data streams and providing anoutput stream); compression and de-compression facilities; analyticfacilities (such as providing automated production of datavisualizations), data processing facilities, and/or data storagefacilities (including storage retention, formatting, compression,migration, etc.), and others.

Services/microservices may include controllers, processors, networkinfrastructure, input/output devices, servers, client devices (e.g.,laptops, desktops, terminals, mobile devices, and/or dedicated devices),sensors (e.g., IoT sensors associated with one or more entities,equipment, and/or collateral), actuators (e.g., automated locks,notification devices, lights, camera controls, etc.), virtualizedversions of any one or more of the foregoing (e.g., outsourced computingresources such as a cloud storage, computing operations; virtualsensors; subscribed data to be gathered such as stock or commodityprices, recordal logs, etc.), and/or include components configured ascomputer readable instructions that, when performed by a processor,cause the processor to perform one or more functions of the service,etc. Services may be distributed across a number of devices, and/orfunctions of a service may be performed by one or more devicescooperating to perform the given function of the service.

Services/microservices may include application programming interfacesthat facilitate connection among the components of the system performingthe service (e.g., microservices) and between the system to entities(e.g., programs, web sites, user devices, etc.) that are external to thesystem. Without limitation to any other aspect of the presentdisclosure, example microservices that may be present in certainembodiments include (a) a multi-modal set of data collection circuitsthat collect information about and monitor entities related to a lendingtransaction; (b) blockchain circuits for maintaining a secure historicalledger of events related to a loan, the blockchain circuits havingaccess control features that govern access by a set of parties involvedin a loan; (c) a set of application programming interfaces, dataintegration services, data processing workflows and user interfaces forhandling loan-related events and loan-related activities; and (d) smartcontract circuits for specifying terms and conditions of smart contractsthat govern at least one of loan terms and conditions, loan-relatedevents and loan-related activities. Any of the services/microservicesmay be controlled by or have control over a controller. Certain systemsmay not be considered to be a service/microservice. For example, a pointof sale device that simply charges a set cost for a good or service maynot be a service. In another example, a service that tracks the cost ofa good or service and triggers notifications when the value changes maynot be a valuation service itself, but may rely on valuation services,and/or may form a portion of a valuation service in certain embodiments.It can be seen that a given circuit, controller, or device may be aservice or a part of a service in certain embodiments, such as when thefunctions or capabilities of the circuit, controller, or device areconfigured to support a service or microservice as described herein, butmay not be a service or part of a service for other embodiments (e.g.,where the functions or capabilities of the circuit, controller, ordevice are not relevant to a service or microservice as describedherein). In another example, a mobile device being operated by a usermay form a portion of a service as described herein at a first point intime (e.g., when the user accesses a feature of the service through anapplication or other communication from the mobile device, and/or when amonitoring function is being performed via the mobile device), but maynot form a portion of the service at a second point in time (e.g., aftera transaction is completed, after the user un-installs an application,and/or when a monitoring function is stopped and/or passed to anotherdevice). Accordingly, the benefits of the present disclosure may beapplied in a wide variety of processes or systems, and any suchprocesses or systems may be considered a service (or a part of aservice) herein.

One of skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, how to combine processes and systemsfrom the present disclosure to construct, provide performancecharacteristics (e.g., bandwidth, computing power, time response, etc.),and/or provide operational capabilities (e.g., time between checks,up-time requirements including longitudinal (e.g., continuous operatingtime) and/or sequential (e.g., time-of-day, calendar time, etc.),resolution and/or accuracy of sensing, data determinations (e.g.,accuracy, timing, amount of data), and/or actuator confirmationcapability) of components of the service that are sufficient to providea given embodiment of a service, platform, and/or microservice asdescribed herein. Certain considerations for the person of skill in theart, in determining the configuration of components, circuits,controllers, and/or devices to implement a service, platform, and/ormicroservice (“service” in the listing following) as described hereininclude, without limitation: the balance of capital costs versusoperating costs in implementing and operating the service; theavailability, speed, and/or bandwidth of network services available forsystem components, service users, and/or other entities that interactwith the service; the response time of considerations for the service(e.g., how quickly decisions within the service must be implemented tosupport the commercial function of the service, the operating time forvarious artificial intelligence or other high computation operations)and/or the capital or operating cost to support a given response time;the location of interacting components of the service, and the effectsof such locations on operations of the service (e.g., data storagelocations and relevant regulatory schemes, network communicationlimitations and/or costs, power costs as a function of the location,support availability for time zones relevant to the service, etc.); theavailability of certain sensor types, the related support for thosesensors, and the availability of sufficient substitutes (e.g., a cameramay require supportive lighting, and/or high network bandwidth or localstorage) for the sensing purpose; an aspect of the underlying value ofan aspect of the service (e.g., a principal amount of a loan, a value ofcollateral, a volatility of the collateral value, a net worth orrelative net worth of a lender, guarantor, and/or borrower, etc.)including the time sensitivity of the underlying value (e.g., if itchanges quickly or slowly relative to the operations of the service orthe term of the loan); a trust indicator between parties of atransaction (e.g., history of performance between the parties, a creditrating, social rating, or other external indicator, conformance ofactivity related to the transaction to an industry standard or othernormalized transaction type, etc.); and/or the availability of costrecovery options (e.g., subscriptions, fees, payment for services, etc.)for given configurations and/or capabilities of the service, platform,and/or microservice. Without limitation to any other aspect of thepresent disclosure, certain operations performed by services hereininclude: performing real-time alterations to a loan based on trackeddata; utilizing data to execute a collateral-backed smart contract;re-evaluating debt transactions in response to a tracked condition ordata, and the like. While specific examples of services/microservicesand considerations are described herein for purposes of illustration,any system benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein, are specifically contemplated within thescope of the present disclosure.

Without limitation, services include a financial service (e.g., a loantransaction service), a data collection service (e.g., a data collectionservice for collecting and monitoring data), a blockchain service (e.g.,a blockchain service to maintain secure data), data integration services(e.g., a data integration service to aggregate data), smart contractservices (e.g., a smart contract service to determine aspects of smartcontracts), software services (e.g., a software service to extract datarelated to the entities from publicly available information sites),crowdsourcing services (e.g., a crowdsourcing service to solicit andreport information), Internet of Things services (e.g., an Internet ofThings service to monitor an environment), publishing services (e.g., apublishing services to publish data), microservices (e.g., having a setof application programming interfaces that facilitate connection amongthe microservices), valuation services (e.g., that use a valuation modelto set a value for collateral based on information), artificialintelligence services, market value data collection services (e.g., thatmonitor and report on marketplace information), clustering services(e.g., for grouping the collateral items based on similarity ofattributes), social networking services (e.g., that enablesconfiguration with respect to parameters of a social network), assetidentification services (e.g., for identifying a set of assets for whicha financial institution is responsible for taking custody), identitymanagement services (e.g., by which a financial institution verifiesidentities and credentials), and the like, and/or similar functionalterminology. Example services to perform one or more functions hereininclude computing devices; servers; networked devices; user interfaces;inter-device interfaces such as communication protocols, sharedinformation and/or information storage, and/or application programminginterfaces (APIs); sensors (e.g., IoT sensors operationally coupled tomonitored components, equipment, locations, or the like); distributedledgers; circuits; and/or computer readable code configured to cause aprocessor to execute one or more functions of the service. One or moreaspects or components of services herein may be distributed across anumber of devices, and/or may consolidated, in whole or part, on a givendevice. In embodiments, aspects or components of services herein may beimplemented at least in part through circuits, such as, in non-limitingexamples, a data collection service implemented at least in part as adata collection circuit structed to collect and monitor data, ablockchain service implemented at least in part as a blockchain circuitstructured to maintain secure data, data integration servicesimplemented at least in part as a data integration circuit structured toaggregate data, smart contract services implemented at least in part asa smart contract circuit structed to determine aspects of smartcontracts, software services implemented at least in part as a softwareservice circuit structured to extract data related to the entities frompublicly available information sites, crowdsourcing services implementedat least in part as a crowdsourcing circuit structured to solicit andreport information, Internet of Things services implemented at least inpart as an Internet of Things circuit structured to monitor anenvironment, publishing services implemented at least in part as apublishing services circuit structured to publish data, microserviceservice implemented at least in part as a microservice circuitstructured to interconnect a plurality of service circuits, valuationservice implemented at least in part as valuation services circuitstructured to access a valuation model to set a value for collateralbased on data, artificial intelligence service implemented at least inpart as an artificial intelligence services circuit, market value datacollection service implemented at least in part as market value datacollection service circuit structured to monitor and report onmarketplace information, clustering service implemented at least in partas a clustering services circuit structured to group collateral itemsbased on similarity of attributes, a social networking serviceimplemented at least in part as a social networking analytic servicescircuit structured to configure parameters with respect to a socialnetwork, asset identification services implemented at least in part asan asset identification service circuit for identifying a set of assetsfor which a financial institution is responsible for taking custody,identity management services implemented at least in part as an identitymanagement service circuit enabling a financial institution to verifyidentities and credentials, and the like. Accordingly, the benefits ofthe present disclosure may be applied in a wide variety of systems, andany such systems may be considered with respect to items and servicesherein, while in certain embodiments a given system may not beconsidered with respect to items and services herein. One of skill inthe art, having the benefit of the disclosure herein and knowledge abouta contemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.Among the considerations that one of skill in the art may contemplate todetermine a configuration for a particular service include: thedistribution and access devices available to one or more parties to aparticular transaction; jurisdictional limitations on the storage, type,and communication of certain types of information; requirements ordesired aspects of security and verification of informationcommunication for the service; the response time of informationgathering, inter-party communications, and determinations to be made byalgorithms, machine learning components, and/or artificial intelligencecomponents of the service; cost considerations of the service, includingcapital expenses and operating costs, as well as which party or entitywill bear the costs and availability to recover costs such as throughsubscriptions, service fees, or the like; the amount of information tobe stored and/or communicated to support the service; and/or theprocessing or computing power to be utilized to support the service.

The terms items and services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, items and service includes anyitems and service, including, without limitation, items and servicesused as a reward, used as collateral, become the subject of anegotiation, and the like, such as, without limitation, an applicationfor a warranty or guarantee with respect to an item that is the subjectof a loan, collateral for a loan, or the like, such as a product, aservice, an offering, a solution, a physical product, software, a levelof service, quality of service, a financial instrument, a debt, an itemof collateral, performance of a service, or other item. Withoutlimitation to any other aspect or description of the present disclosure,items and service includes any items and service, including, withoutlimitation, items and services as applied to physical items (e.g., avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty), a financial item (e.g., a commodity, a security, a currency,a token of value, a ticket, a cryptocurrency), a consumable item (e.g.,an edible item, a beverage), a highly valued item (e.g., a preciousmetal, an item of jewelry, a gemstone), an intellectual item (e.g., anitem of intellectual property, an intellectual property right, acontractual right), and the like. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered with respect to items and servicesherein, while in certain embodiments a given system may not beconsidered with respect to items and services herein. One of skill inthe art, having the benefit of the disclosure herein and knowledge abouta contemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.

The terms agent, automated agent, and similar terms as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, an agent or automated agent mayprocess events relevant to at least one of the value, the condition, andthe ownership of items of collateral or assets. The agent or automatedagent may also undertake an action related to a loan, debt transaction,bond transaction, subsidized loan, or the like to which the collateralor asset is subject, such as in response to the processed events. Theagent or automated agent may interact with a marketplace for purposes ofcollecting data, testing spot market transactions, executingtransactions, and the like, where dynamic system behavior involvescomplex interactions that a user may desire to understand, predict,control, and/or optimize. Certain systems may not be considered an agentor an automated agent. For example, if events are merely collected butnot processed, the system may not be an agent or automated agent. Insome embodiments, if a loan-related action is undertaken not in responseto a processed event, it may not have been undertaken by an agent orautomated agent. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure include and/or benefit from agents or automatedagent. Certain considerations for the person of skill in the art, orembodiments of the present disclosure with respect to an agent orautomated agent include, without limitation: rules that determine whenthere is a change in a value, condition or ownership of an asset orcollateral, and/or rules to determine if a change warrants a furtheraction on a loan or other transaction, and other considerations. Whilespecific examples of market values and marketplace information aredescribed herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein are specifically contemplated within the scope of thepresent disclosure.

The term marketplace information, market value and similar terms asutilized herein should be understood broadly. Without limitation to anyother aspect or description of the present disclosure, marketplaceinformation and market value describes a status or value of an asset,collateral, food, or service at a defined point or period in time.Market value may refer to the expected value placed on an item in amarketplace or auction setting, or pricing or financial data for itemsthat are similar to the item, asset, or collateral in at least onepublic marketplace. For a company, market value may be the number of itsoutstanding shares multiplied by the current share price. Valuationservices may include market value data collection services that monitorand report on marketplace information relevant to the value (e.g. marketvalue) of collateral, the issuer, a set of bonds, and a set of assets. aset of subsidized loans, a party, and the like. Market values may bedynamic in nature because they depend on an assortment of factors, fromphysical operating conditions to economic climate to the dynamics ofdemand and supply. Market value may be affected by, and marketplaceinformation may include, proximity to other assets, inventory or supplyof assets, demand for assets, origin of items, history of items,underlying current value of item components, a bankruptcy condition ofan entity, a foreclosure status of an entity, a contractual defaultstatus of an entity, a regulatory violation status of an entity, acriminal status of an entity, an export controls status of an entity, anembargo status of an entity, a tariff status of an entity, a tax statusof an entity, a credit report of an entity, a credit rating of anentity, a web site rating of an entity, a set of customer reviews for aproduct of an entity, a social network rating of an entity, a set ofcredentials of an entity, a set of referrals of an entity, a set oftestimonials for an entity, a set of behavior of an entity, a locationof an entity, and a geolocation of an entity. In certain embodiments, amarket value may include information such as a volatility of a value, asensitivity of a value (e.g., relative to other parameters having anuncertainty associated therewith), and/or a specific value of thevaluated object to a particular party (e.g., an object may have morevalue as possessed by a first party than as possessed by a secondparty).

Certain information may not be marketplace information or a marketvalue. For example, where variables related to a value are notmarket-derived, they may be a value-in-use or an investment value. Incertain embodiments, an investment value may be considered a marketvalue (e.g., when the valuating party intends to utilize the asset as aninvestment if acquired), and not a market value in other embodiments(e.g., when the valuating party intends to immediately liquidate theinvestment if acquired). One of skill in the art, having the benefit ofthe disclosure herein and knowledge about a contemplated systemordinarily available to that person, can readily determine which aspectsof the present disclosure will benefit from marketplace information or amarket value. Certain considerations for the person of skill in the art,in determining whether the term market value is referring to an asset,item, collateral, good, or service include: the presence of othersimilar assets in a marketplace, the change in value depending onlocation, an opening bid of an item exceeding a list price, and otherconsiderations. While specific examples of market values and marketplaceinformation are described herein for purposes of illustration, anyembodiment benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein are specifically contemplated within the scopeof the present disclosure.

The term apportion value or apportioned value and similar terms asutilized herein should be understood broadly. Without limitation to anyother aspect or description of the present disclosure, apportion valuedescribes a proportional distribution or allocation of valueproportionally, or a process to divide and assign value according to arule of proportional distribution. Apportionment of the value may be toseveral parties (e.g., each of the several parties is a beneficiary of aportion of the value), to several transactions (e.g., each of thetransactions utilizes a portion of the value), and/or in a many-to-manyrelationship (e.g., a group of objects has an aggregate value that isapportioned between a number of parties and/or transactions). In someembodiments, the value may be a net loss and the apportioned value isthe allocation of a liability to each entity. In other embodiments,apportioned value may refer to the distribution or allocation of aneconomic benefit, real estate, collateral, or the like. In certainembodiments, apportionment may include a consideration of the valuerelative to the parties—for example, a $10 million asset apportioned50/50 between two parties, where the parties have distinct valueconsiderations for the asset, may result in one party crediting theapportionment differing resulting values from the apportionment. Incertain embodiments, apportionment may include a consideration of thevalue relative to given transactions—for example, a first type oftransaction (e.g., a long-term loan) may have a different valuation of agiven asset than a second type of transaction (e.g., a short-term lineof credit).

Certain conditions or processes may not relate to apportioned value. Forexample, the total value of an item may provide its inherent worth, butnot how much of the value is held by each identified entity. One ofskill in the art, having the benefit of the disclosure herein andknowledge about apportioned value, can readily determine which aspectsof the present disclosure will benefit a particular application forapportioned value. Certain considerations for the person of skill in theart, or embodiments of the present disclosure with respect to anapportioned value include, without limitation: the currency of theprincipal sum, the anticipated transaction type (loan, bond or debt),the specific type of collateral, the ratio of the loan to value, theratio of the collateral to the loan, the gross transaction/loan amount,the amount of the principal sum, the number of entities owed, the valueof the collateral, and the like. While specific examples of apportionedvalues are described herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein are specifically contemplated within the scope of thepresent disclosure.

The term financial condition and similar terms as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, financial condition describes acurrent status of an entity's assets, liabilities, and equity positionsat a defined point or period in time. The financial condition may bememorialized in financial statement. The financial condition may furtherinclude an assessment of the ability of the entity to survive futurerisk scenarios or meet future or maturing obligations. Financialcondition may be based on a set of attributes of the entity selectedfrom among a publicly stated valuation of the entity, a set of propertyowned by the entity as indicated by public records, a valuation of a setof property owned by the entity, a bankruptcy condition of an entity, aforeclosure status of an entity, a contractual default status of anentity, a regulatory violation status of an entity, a criminal status ofan entity, an export controls status of an entity, an embargo status ofan entity, a tariff status of an entity, a tax status of an entity, acredit report of an entity, a credit rating of an entity, a web siterating of an entity, a set of customer reviews for a product of anentity, a social network rating of an entity, a set of credentials of anentity, a set of referrals of an entity, a set of testimonials for anentity, a set of behavior of an entity, a location of an entity, and ageolocation of an entity. A financial condition may also describe arequirement or threshold for an agreement or loan. For example,conditions for allowing a developer to proceed may be variouscertifications and their agreement to a financial payout. That is, thedeveloper's ability to proceed is conditioned upon a financial element,among others. Certain conditions may not be a financial condition. Forexample, a credit card balance alone may be a clue as to the financialcondition, but may not be the financial condition on its own. In anotherexample, a payment schedule may determine how long a debt may be on anentity's balance sheet, but in a silo may not accurately provide afinancial condition. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure include and/or will benefit from a financialcondition. Certain considerations for the person of skill in the art, indetermining whether the term financial condition is referring to acurrent status of an entity's assets, liabilities, and equity positionsat a defined point or period in time and/or for a given purpose include:the reporting of more than one financial data point, the ratio of a loanto value of collateral, the ratio of the collateral to the loan, thegross transaction/loan amount, the credit scores of the borrower and thelender, and other considerations. While specific examples of financialconditions are described herein for purposes of illustration, anyembodiment benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein are specifically contemplated within the scopeof the present disclosure.

The term interest rate and similar terms, as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, interest rate includes an amountof interest due per period, as a proportion of an amount lent,deposited, or borrowed. The total interest on an amount lent or borrowedmay depend on the principal sum, the interest rate, the compoundingfrequency, and the length of time over which it is lent, deposited, orborrowed. Typically, interest rate is expressed as an annual percentagebut can be defined for any time period. The interest rate relates to theamount a bank or other lender charges to borrow its money, or the rate abank or other entity pays its savers for keeping money in an account.Interest rate may be variable or fixed. For example, an interest ratemay vary in accordance with a government or other stakeholder directive,the currency of the principal sum lent or borrowed, the term to maturityof the investment, the perceived default probability of the borrower,supply and demand in the market, the amount of collateral, the status ofan economy, or special features like call provisions. In certainembodiments, an interest rate may be a relative rate (e.g., relative toa prime rate, an inflation index, etc.). In certain embodiments, aninterest rate may further consider costs or fees applied (e.g.,“points”) to adjust the interest rate. A nominal interest rate may notbe adjusted for inflation while a real interest rate takes inflationinto account. Certain examples may not be an interest rate for purposesof particular embodiments. For example, a bank account growing by afixed dollar amount each year, and/or a fixed fee amount, may not be anexample of an interest rate for certain embodiments. One of skill in theart, having the benefit of the disclosure herein and knowledge aboutinterest rates, can readily determine the characteristics of an interestrate for a particular embodiment. Certain considerations for the personof skill in the art, or embodiments of the present disclosure withrespect to an interest rate include, without limitation: the currency ofthe principal sum, variables for setting an interest rate, criteria formodifying an interest rate, the anticipated transaction type (loan, bondor debt), the specific type of collateral, the ratio of the loan tovalue, the ratio of the collateral to the loan, the grosstransaction/loan amount, the amount of the principal sum, theappropriate lifespans of transactions and/or collateral for a particularindustry, the likelihood that a lender will sell and/or consolidate aloan before the term, and the like. While specific examples of interestrates are described herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein are specifically contemplated within the scope of thepresent disclosure.

The term valuation services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a valuation service includes anyservice that sets a value for a good or service. Valuation services mayuse a valuation model to set a value for collateral based on informationfrom data collection and monitoring services. Smart contract servicesmay process output from the set of valuation services and assign itemsof collateral sufficient to provide security for a loan and/or apportionvalue for an item of collateral among a set of lenders and/ortransactions. Valuation services may include artificial intelligenceservices that may iteratively improve the valuation model based onoutcome data relating to transactions in collateral. Valuation servicesmay include market value data collection services that may monitor andreport on marketplace information relevant to the value of collateral.Certain processes may not be considered to be a valuation service. Forexample, a point of sale device that simply charges a set cost for agood or service may not be a valuation service. In another example, aservice that tracks the cost of a good or service and triggersnotifications when the value changes may not be a valuation serviceitself, but may rely on valuation services and/or form a part of avaluation service. Accordingly, the benefits of the present disclosuremay be applied in a wide variety of processes systems, and any suchprocesses or systems may be considered a valuation service herein, whilein certain embodiments a given service may not be considered a valuationservice herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system and how to combineprocesses and systems from the present disclosure to enhance operationsof the contemplated system and/or to provide a valuation service.Certain considerations for the person of skill in the art, indetermining whether a contemplated system is a valuation service and/orwhether aspects of the present disclosure can benefit or enhance thecontemplated system include, without limitation: perform real-timealterations to a loan based on a value of a collateral; utilizemarketplace data to execute a collateral-backed smart contract;re-evaluate collateral based on a storage condition or geolocation; thetendency of the collateral to have a volatile value, be utilized, and/orbe moved; and the like. While specific examples of valuation servicesand considerations are described herein for purposes of illustration,any system benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein, are specifically contemplated within thescope of the present disclosure.

The term collateral attributes (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, collateral attributes include anyidentification of the durability (ability of the collateral to withstandwear or the useful life of the collateral), value, identification (doesthe collateral have definite characteristics that make it easy toidentify or market), stability of value (does the collateral maintainvalue over time), standardization, grade, quality, marketability,liquidity, transferability, desirability, trackability, deliverability(ability of the collateral be delivered or transfer without adeterioration in value), market transparency (is the collateral valueeasily verifiable or widely agreed upon), physical or virtual.Collateral attributes may be measured in absolute or relative terms,and/or may include qualitative (e.g., categorical descriptions) orquantitative descriptions. Collateral attributes may be different fordifferent industries, products, elements, uses, and the like. Collateralattributes may be assigned quantitative or qualitative values. Valuesassociated with collateral attributes may be based on a scale (such as1-10) or a relative designation (high, low, better, etc.). Collateralmay include various components; each component may have collateralattributes. Collateral may, therefore, have multiple values for the samecollateral attribute. In some embodiments, multiple values of collateralattributes may be combined to generate one value for each attribute.Some collateral attributes may apply only to specific portions ofcollateral. Some collateral attributes, even for a given component ofthe collateral, may have distinct values depending upon the party ofinterest (e.g., a party that values an aspect of the collateral morehighly than another party) and/or depending upon the type of transaction(e.g., the collateral may be more valuable or appropriate for a firsttype of loan than for a second type of loan). Certain attributesassociated with collateral may not be collateral attributes as describedherein depending upon the purpose of the collateral attributes herein.For example, a product may be rated as durable relative to similarproducts; however, if the life of the product is much lower than theterm of a particular loan in consideration, the durability of theproduct may be rated differently (e.g., not durable) or irrelevant(e.g., where the current inventory of the product is attached as thecollateral, and is expected to change out during the term of the loan).Accordingly, the benefits of the present disclosure may be applied to avariety of attributes, and any such attributes may be consideredcollateral attributes herein, while in certain embodiments a givenattribute may not be considered a collateral attribute herein. One ofskill in the art, having the benefit of the disclosure herein andknowledge about contemplated collateral attributes ordinarily availableto that person, can readily determine which aspects of the presentdisclosure will benefit a particular collateral attribute. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated attribute is a collateral attribute and/orwhether aspects of the present disclosure can benefit or enhance thecontemplated system include, without limitation: the source of theattribute and the source of the value of the attribute (e.g. does theattribute and attribute value comes from a reputable source), thevolatility of the attribute (e.g. does the attribute values for thecollateral fluctuate, is the attribute a new attribute for thecollateral), relative differences in attribute values for similarcollateral, exceptional values for attributes (e.g., some attributevalues may be high, such as, in the 98th percentile or very low, such asin the 2nd percentile, compared to similar class of collateral), thefungibility of the collateral, the type of transaction related to thecollateral, and/or the purpose of the utilization of collateral for aparticular party or transaction. While specific examples of collateralattributes and considerations are described herein for purposes ofillustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term blockchain services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, blockchain services includes anyservice related to the processing, recordation, and/or updating of ablockchain, and may include services for processing blocks, computinghash values, generating new blocks in a blockchain, appending a block tothe blockchain, creating a fork in the blockchain, merging of forks inthe blockchain, verifying previous computations, updating a sharedledger, updating a distributed ledger, generating cryptographic keys,verifying transactions, maintaining a blockchain, updating a blockchain,verifying a blockchain, generating random numbers. The services may beperformed by execution of computer readable instructions on localcomputers and/or by remote servers and computers. Certain services maynot be considered blockchain services individually but may be consideredblockchain services based on the final use of the service and/or in aparticular embodiment—for example, a computing a hash value may beperformed in a context outside of a blockchain such in the context ofsecure communication. Some initial services may be invoked without firstbeing applied to blockchains, but further actions or services inconjunction with the initial services may associate the initial servicewith aspects of blockchains. For example, a random number may beperiodically generated and stored in memory; the random numbers mayinitially not be generated for blockchain purposes but may be utilizedfor blockchains. Accordingly, the benefits of the present disclosure maybe applied in a wide variety of services, and any such services may beconsidered blockchain services herein, while in certain embodiments agiven service may not be considered a blockchain service herein. One ofskill in the art, having the benefit of the disclosure herein andknowledge about a contemplated blockchain service ordinarily availableto that person, can readily determine which aspects of the presentdisclosure can be configured to implement, and/or will benefit, aparticular blockchain service. Certain considerations for the person ofskill in the art, in determining whether a contemplated service is ablockchain service and/or whether aspects of the present disclosure canbenefit or enhance the contemplated system include, without limitation:the application of the service, the source of the service (e.g., if theservice is associated with a known or verifiable blockchain serviceprovider), responsiveness of the service (e.g., some blockchain servicesmay have an expected completion time, and/or may be determined throughutilization), cost of the service, the amount of data requested for theservice, and/or the amount of data generated by the service (blocks ofblockchain or keys associated with blockchains may be a specific size ora specific range of sizes). While specific examples of blockchainservices and considerations are described herein for purposes ofillustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term blockchain (and variations such as cryptocurrency ledger, andthe like) as utilized herein may be understood broadly to describe acryptocurrency ledger that records, administrates, or otherwiseprocesses online transactions. A blockchain may be public, private, or acombination thereof, without limitation. A blockchain may also be usedto represent a set of digital transactions, agreement, terms, or otherdigital value. Without limitation to any other aspect or description ofthe present disclosure, in the former case, a blockchain may also beused in conjunction with investment applications, token-tradingapplications, and/or digital/cryptocurrency based marketplaces. Ablockchain can also be associated with rendering consideration, such asproviding goods, services, items, fees, access to a restricted area orevent, data, or other valuable benefit. Blockchains in various forms maybe included where discussing a unit of consideration, collateral,currency, cryptocurrency, or any other form of value. One of skill inthe art, having the benefit of the disclosure herein and knowledgeordinarily available about a contemplated system, can readily determinethe value symbolized or represented by a blockchain. While specificexamples of blockchains are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms ledger and distributed ledger (and similar terms) as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, a ledger may be adocument, file, computer file, database, book, and the like whichmaintains a record of transactions. Ledgers may be physical or digital.Ledgers may include records related to sales, accounts, purchases,transactions, assets, liabilities, incomes, expenses, capital, and thelike. Ledgers may provide a history of transactions that may beassociated with time. Ledgers may be centralized ordecentralized/distributed. A centralized ledger may be a document thatis controlled, updated, or viewable by one or more selected entities ora clearinghouse and wherein changes or updates to the ledger aregoverned or controlled by the entity or clearinghouse. A distributedledger may be a ledger that is distributed across a plurality ofentities, participants or regions which may independently, concurrently,or consensually, update, or modify their copies of the ledger. Ledgersand distributed ledgers may include security measures and cryptographicfunctions for signing, concealing, or verifying content. In the case ofdistributed ledgers, blockchain technology may be used. In the case ofdistributed ledgers implemented using blockchain, the ledger may beMerkle trees comprising a linked list of nodes in which each nodecontains hashed or encrypted transactional data of the previous nodes.Certain records of transactions may not be considered ledgers. A file,computer file, database, or book may or may not be a ledger depending onwhat data it stores, how the data is organized, maintained, or secured.For example, a list of transactions may not be considered a ledger if itcannot be trusted or verified, and/or if it is based on inconsistent,fraudulent, or incomplete data. Data in ledgers may be organized in anyformat such as tables, lists, binary streams of data, or the like whichmay depend on convenience, source of data, type of data, environment,applications, and the like. A ledger that is shared among variousentities may not be a distributed ledger, but the distinction ofdistributed may be based on which entities are authorized to makechanges to the ledger and/or how the changes are shared and processedamong the different entities. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of data, and any such datamay be considered ledgers herein, while in certain embodiments a givendata may not be considered a ledger herein. One of skill in the art,having the benefit of the disclosure herein and knowledge aboutcontemplated ledgers and distributed ledger ordinarily available to thatperson, can readily determine which aspects of the present disclosurecan be utilized to implement, and/or will benefit a particular ledger.Certain considerations for the person of skill in the art, indetermining whether a contemplated data is a ledger and/or whetheraspects of the present disclosure can benefit or enhance thecontemplated ledger include, without limitation: the security of thedata in the ledger (can the data be tampered or modified), the timeassociated with making changes to the data in the ledger, cost of makingchanges (computationally and monetarily), detail of data, organizationof data (does the data need to be processed for use in an application),who controls the ledger (can the party be trusted or relied to managethe ledger), confidentiality of the data (who can see or track the datain the ledger), size of the infrastructure, communication requirements(distributed ledgers may require a communication interface or specificinfrastructure), resiliency. While specific examples of blockchainservices and considerations are described herein for purposes ofillustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term loan (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a loan may be an agreementrelated to an asset that is borrowed, and that is expected to bereturned in kind (e.g., money borrowed and money returned) or as anagreed transaction (e.g., a first good or service is borrowed, andmoney, a second good or service, or a combination, is returned). Assetsmay be money, property, time, physical objects, virtual objects,services, a right (e.g., a ticket, a license, or other right), adepreciation amount, a credit (e.g., a tax credit, an emissions credit,etc.), an agreed assumption of a risk or liability, and/or anycombination thereof. A loan may be based on a formal or informalagreement between a borrower and a lender wherein a lender may providean asset to the borrower for a predefined amount of time, a variableperiod of time, or indefinitely. Lenders and borrowers may beindividuals, entities, corporations, governments, groups of people,organizations, and the like. Loan types may include mortgage loans,personal loans, secured loans, unsecured loans, concessional loans,commercial loans, microloans, and the like. The agreement between theborrower and the lender may specify terms of the loan. The borrower maybe required to return an asset or repay with a different asset than wasborrowed. In some cases, a loan may require interest to be repaid on theborrowed asset. Borrowers and lenders may be intermediaries betweenother entities and may never possess or use the asset. In someembodiments, a loan may not be associated with direct transfer of goodsbut may be associated with usage rights or shared usage rights. Incertain embodiments, the agreement between the borrower and the lendermay be executed between the borrower and the lender, and/or executedbetween an intermediary (e.g., a beneficiary of a loan right such asthrough a sale of the loan). In certain embodiment, the agreementbetween the borrower and the lender may be executed through servicesherein, such as through a smart contract service that determines atleast a portion of the terms and conditions of the loans, and in certainembodiments may commit the borrower and/or the lender to the terms ofthe agreement, which may be a smart contract. In certain embodiments,the smart contract service may populate the terms of the agreement, andpresent them to the borrower and/or lender for execution. In certainembodiments, the smart contract service may automatically commit one ofthe borrower or the lender to the terms (at least as an offer), and maypresent the offer to the other one of the borrower or the lender forexecution. In certain embodiments, a loan agreement may include multipleborrowers and/or multiple lenders, for example where a set of loansincludes a number of beneficiaries of payment on the set of loans,and/or a number of borrowers on the set of loans. In certainembodiments, the risks and/or obligations of the set of loans may beindividualized (e.g., each borrower and/or lender is related to specificloans of the set of loans), apportioned (e.g., a default on a particularloan has an associated loss apportioned between the lenders), and/orcombinations of these (e.g., one or more subsets of the set of loans istreated individually and/or apportioned).

Certain agreements may not be considered a loan. An agreement totransfer or borrow assets may not be a loan depending on what assets aretransferred, how the assets were transferred, or the parties involved.For example, in some cases, the transfer of assets may be for anindefinite time and may be considered a sale of the asset or a permanenttransfer. Likewise, if an asset is borrowed or transferred without clearor definite terms or lack of consensus between the lender and theborrower it may, in some cases, not be considered a loan. An agreementmay be considered a loan even if a formal agreement is not directlycodified in a written agreement as long as the parties willingly andknowingly agreed to the arrangement, and/or ordinary practices (e.g., ina particular industry) may treat the transaction as a loan. Accordingly,the benefits of the present disclosure may be applied in a wide varietyof agreements, and any such agreement may be considered a loan herein,while in certain embodiments a given agreement may not be considered aloan herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about contemplated loans ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure implement a loan, utilize a loan, or benefit aparticular loan transaction. Certain considerations for the person ofskill in the art, in determining whether a contemplated data is a loanand/or whether aspects of the present disclosure can benefit or enhancethe contemplated loan include, without limitation: the value of theassets involved, the ability of the borrower to return or repay theloan, the types of assets involved (e.g., whether the asset is consumedthrough utilization), the repayment time frame associated with the loan,the interest on the loan, how the agreement of the loan was arranged,formality of the agreement, detail of the agreement, the detail of theagreements of the loan, the collateral attributes associated with theloan, and/or the ordinary business expectations of any of the foregoingin a particular context. While specific examples of loans andconsiderations are described herein for purposes of illustration, anysystem benefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term loan related event(s) (and similar terms, includingloan-related events) as utilized herein should be understood broadly.Without limitation to any other aspect or description of the presentdisclosure, a loan related events may include any event related to termsof the loan or events triggered by the agreement associated with theloan. Loan-related events may include default on loan, breach ofcontract, fulfillment, repayment, payment, change in interest, late feeassessment, refund assessment, distribution, and the like. Loan-relatedevents may be triggered by explicit agreement terms; for example—anagreement may specify a rise in interest rate after a time period haselapsed from the beginning of the loan; the rise in interest ratetriggered by the agreement may be a loan related event. Loan-relatedevents may be triggered implicitly by related loan agreement terms. Incertain embodiments, any occurrence that may be considered relevant toassumptions of the loan agreement, and/or expectations of the parties tothe loan agreement, may be considered an occurrence of an event. Forexample, if collateral for a loan is expected to be replaceable (e.g.,an inventory as collateral), then a change in inventory levels may beconsidered an occurrence of a loan related event. In another example, ifreview and/or confirmation of the collateral is expected, then a lack ofaccess to the collateral, the disablement or failure of a monitoringsensor, etc. may be considered an occurrence of a loan related event. Incertain embodiments, circuits, controllers, or other devices describedherein may automatically trigger the determination of a loan-relatedevents. In some embodiments, loan-related events may be triggered byentities that manage loans or loan-related contracts. Loan-relatedevents may be conditionally triggered based on one or more conditions inthe loan agreement. Loan related events may be related to tasks orrequirements that need to be completed by the lender, borrower, or athird party. Certain events may be considered loan-related events incertain embodiments and/or in certain contexts, but may not beconsidered a loan-related event in another embodiment or context. Manyevents may be associated with loans but may be caused by externaltriggers not associated with a loan. However, in certain embodiments, anexternally triggered event (e.g., a commodity price change related to acollateral item) may be loan-related events in certain embodiments. Forexample, renegotiation of loan terms initiated by a lender may not beconsidered a loan related event if the terms and/or performance of theexisting loan agreement did not trigger the renegotiation. Accordingly,the benefits of the present disclosure may be applied in a wide varietyof events, and any such event may be considered a loan related eventherein, while in certain embodiments given events may not be considereda loan related event herein. One of skill in the art, having the benefitof the disclosure herein and knowledge about a contemplated systemordinarily available to that person, can readily determine which aspectsof the present disclosure may be considered a loan-related event for thecontemplated system and/or for particular transactions supported by thesystem. Certain considerations for the person of skill in the art, indetermining whether a contemplated data is a loan related event and/orwhether aspects of the present disclosure can benefit or enhance thecontemplated transaction system include, without limitation: the impactof the related event on the loan (events that cause default ortermination of the loan may have higher impact), the cost (capitaland/or operating) associated with the event, the cost (capital and/oroperating) associated with monitoring for an occurrence of the event,the entities responsible for responding to the event, a time periodand/or response time associated with the event (e.g., time required tocomplete the event and time that is allotted from the time the event istriggered to when processing or detection of the event is desired tooccur), the entity responsible for the event, the data required forprocessing the event (e.g., confidential information may have differentsafeguards or restrictions), the availability of mitigating actions ifan undetected event occurs, and/or the remedies available to an at-riskparty if the event occurs without detection. While specific examples ofloan-related events and considerations are described herein for purposesof illustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term loan-related activities (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a loan related activity mayinclude activities related to the generation, maintenance, termination,collection, enforcement, servicing, billing, marketing, ability toperform, or negotiation of a loan. Loan-related activity may includeactivities related to the signing of a loan agreement or a promissorynote, review of loan documents, processing of payments, evaluation ofcollateral, evaluation of compliance of the borrower or lender to theloan terms, renegotiation of terms, perfection of security or collateralfor the loan, and/or a negation of terms. Loan-related activities mayrelate to events associated with a loan before formal agreement on theterms, such as activities associated with initial negotiations.Loan-related activities may relate to events during the life of the loanand after the termination of a loan. Loan-related activities may beperformed by a lender, borrower, or a third party. Certain activitiesmay not be considered loan related activities services individually butmay be considered loan related activities based on the specificity ofthe activity to the loan lifecycle—for example, billing or invoicingrelated to outstanding loans may be considered a loan related activity,however when the invoicing or billing of loans is combined with billingor invoicing for non loan-related elements the invoicing may not beconsidered a loan related activity. Some activities may be performed inrelation to an asset regardless if a loan is associated with the asset;in these cases, the activity may not be considered a loan relatedactivity. For example, regular audits related to an asset may occurregardless if the asset is associated with a loan and may not beconsidered a loan related activity. In another example, a regular auditrelated to an asset may be required by a loan agreement and would nottypically occur but for the association with a loan, in this case, theactivity may be considered a loan related activity. In some embodiments,activities may be considered loan-related activities if the activitywould otherwise not occur if the loan were not active or present, butmay still be considered a loan-related activity in some instances (e.g.,if auditing occurs normally, but the lender does not have the ability toenforce or review the audit, then the audit may be considered aloan-related activity even though it already occurs otherwise).Accordingly, the benefits of the present disclosure may be applied in awide variety of events, and any such event may be considered a loanrelated event herein, while in certain embodiments given events may notbe considered a loan related events herein. One of skill in the art,having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine a loan related activity for the purposes of the contemplatedsystem. Certain considerations for the person of skill in the art, indetermining whether a contemplated data is a loan related activityand/or whether aspects of the present disclosure can benefit or enhancethe contemplated loan include, without limitation: the necessity of theactivity for the loan (can the loan agreement or terms be satisfiedwithout the activity), the cost of the activity, the specificity of theactivity to the loan (is the activity similar or identical to otherindustries), time involved in the activity, the impact of the activityon a loan life cycle, entity performing the activity, amount of datarequired for the activity (does the activity require confidentialinformation related to the loan, or personal information related to theentities), and/or the ability of parties to enforce and/or review theactivity. While specific examples of loan-related events andconsiderations are described herein for purposes of illustration, anysystem benefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The terms loan-terms, loan terms, terms for a loan, terms andconditions, and the like as utilized herein should be understood broadly(“loan terms”). Without limitation to any other aspect or description ofthe present disclosure, loan terms may relate to conditions, rules,limitations, contract obligations, and the like related to the timing,repayment, origination, and other enforceable conditions agreed to bythe borrower and the lender of the loan. Loan terms may be specified ina formal contract between a borrower and the lender. Loan terms mayspecify aspects of an interest rate, collateral, foreclose conditions,consequence of debt, payment options, payment schedule, a covenant, andthe like. Loan terms may be negotiable or may change during the life ofa loan. Loan terms may be change or be affected by outside parameterssuch as market prices, bond prices, conditions associated with a lenderor borrower, and the like. Certain aspects of a loan may not beconsidered loan terms. In certain embodiments, aspects of loan that havenot been formally agreed upon between a lender and a borrower, and/orthat are not ordinarily understood in the course of business (and/or theparticular industry) may not be considered loan terms. Certain aspectsof a loan may be preliminary or informal until they have been formallyagreed or confirmed in a contract or a formal agreement. Certain aspectsof a loan may not be considered loan terms individually but may not beconsidered loan terms based on the specificity of the aspect to aspecific loan. Certain aspects of a loan may not be considered loanterms at a particular time during the loan, but may be considered loanterms at another time during the loan (e.g., obligations and/or waiversthat may occur through the performance of the parties, and/or expirationof a loan term). For example, an interest rate may generally not beconsidered a loan term until it is defined in relation of a loan anddefined as to how the interest compounded (annual, monthly), calculated,and the like. An aspect of a loan may not be considered a term if it isindefinite or unenforceable. Some aspects may be manifestations orrelated to terms of a loan but may themselves not be the terms. Forexample, a loan term be the repayment period of a loan, such as oneyear. The term may not specify how the loan is to be repaid in the year.The loan may be repaid with 12 monthly payments or one annual payment. Amonthly payment plan in this case may not be considered a loan term asit just one or many options for repayment not directly specified by aloan. Accordingly, the benefits of the present disclosure may be appliedin a wide variety of loan aspects, and any such aspect may be considereda loan term herein, while in certain embodiments given aspects may notbe considered loan terms herein. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure are loan terms for the contemplatedsystem.

Certain considerations for the person of skill in the art, indetermining whether a contemplated data is a loan term and/or whetheraspects of the present disclosure can benefit or enhance thecontemplated loan include, without limitation: the enforceability of theterms (can the conditions be enforced by the lender or the lender or theborrower), the cost of enforcing the terms (amount of time, or effortrequired ensure the conditions are being followed), the complexity ofthe terms (how easily can they be followed or understood by the partiesinvolved, are the terms error prone or easily misunderstood), entitiesresponsible for the terms, fairness of the terms, stability of the terms(how often do they change), observability of the terms (can the terms beverified by a another party), favorability of the terms to one party (dothe terms favor the borrower or the lender), risk associated with theloan (terms may depend on the probability that the loan may not berepaid), characteristics of the borrower or lender (their ability tomeet the terms), and/or ordinary expectations for the loan and/orrelated industry.

While specific examples of loan terms are described herein for purposesof illustration, any system benefitting from the disclosures herein, andany considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term loan conditions, loan-conditions, conditions for a loan, termsand conditions, and the like as utilized herein should be understoodbroadly (“loan conditions”). Without limitation to any other aspect ordescription of the present disclosure, loan conditions may relate torules, limits, and/or obligations related to a loan. Loan conditions mayrelate to rules or necessary obligations for obtaining a loan, formaintaining a loan, for applying for a loan, for transferring a loan,and the like. Loan conditions may include principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, treatment of collateral, access to collateral, a party, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition,conditions related to other debts of the borrower, and a consequence ofdefault.

Certain aspects of a loan may not be considered loan conditions. Aspectsof loan that have not been formally agreed upon between a lender and aborrower, and/or that are not ordinarily understood in the course ofbusiness (and/or the particular industry), may not be considered loanconditions. Certain aspects of a loan may be preliminary or informaluntil they have been formally agreed or confirmed in a contract or aformal agreement. Certain aspects of a loan may not be considered loanconditions individually but may be considered loan conditions based onthe specificity of the aspect to a specific loan. Certain aspects of aloan may not be considered loan conditions at a particular time duringthe loan, but may be considered loan conditions at another time duringthe loan (e.g., obligations and/or waivers that may occur through theperformance of the parties, and/or expiration of a loan condition).Accordingly, the benefits of the present disclosure may be applied in awide variety of loan aspects, and any such aspect may be considered loanconditions herein, while in certain embodiments given aspects may not beconsidered loan conditions herein. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure are loan conditions for thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated data is a loan conditionand/or whether aspects of the present disclosure can benefit or enhancethe contemplated loan include, without limitation: the enforceability ofthe condition (can the conditions be enforced by the lender or theborrower), the cost of enforcing the condition (amount of time, oreffort required ensure the conditions are being followed), thecomplexity of the condition (how easily can they be followed orunderstood by the parties involved, are the conditions error prone oreasily misunderstood), entities responsible for the conditions, fairnessof the conditions, observability of the conditions (can the conditionsbe verified by a another party), favorability of the conditions to oneparty (do the conditions favor the borrower or the lender), riskassociated with the loan (conditions may depend on the probability thatthe loan may not be repaid), and/or ordinary expectations for the loanand/or related industry.

While specific examples of loan conditions are described herein forpurposes of illustration, any system benefitting from the disclosuresherein, and any considerations understood to one of skill in the arthaving the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term loan collateral, collateral, item of collateral, collateralitem, and the like as utilized herein should be understood broadly.Without limitation to any other aspect or description of the presentdisclosure, a loan collateral may relate to any asset or property that aborrower promises to a lender as backup in exchange for a loan, and/oras security for the loan. Collateral may be any item of value that isaccepted as an alternate form of repayment in case of default on a loan.Collateral may include any number of physical or virtual items such as avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty. Collateral may include more than one item or types of items.

A collateral item may describe an asset, a property, a value, or otheritem defined as a security for a loan or a transaction. A set ofcollateral items may be defined, and within that set substitution,removal or addition of collateral items may be effected. For example, acollateral item may be, without limitation: a vehicle, a ship, a plane,a building, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, or an item of personal property, or the like. If aset or plurality of collateral items is defined, substitution, removalor addition of collateral items may be effected, such as substituting,removing or adding a collateral item to or from a set of collateralitems. Without limitation to any other aspect or description of thepresent disclosure, a collateral item or set of collateral items mayalso be used in conjunction with other terms to an agreement or loan,such as a representation, a warranty, an indemnity, a covenant, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a security, a personal guarantee, a lien, a duration, aforeclose condition, a default condition, and a consequence of default.In certain embodiments, a smart contract may calculate whether aborrower has satisfied conditions or covenants and in cases where theborrower has not satisfied such conditions or covenants, may enableautomated action or trigger another conditions or terms that may affectthe status, ownership or transfer of a collateral item, or initiate thesubstitution, removal, or addition of collateral items to a set ofcollateral for a loan. One of skill in the art, having the benefit ofthe disclosure herein and knowledge about collateral items, can readilydetermine the purposes and use of collateral items in variousembodiments and contexts disclosed herein, including the substitution,removal, and addition thereof.

While specific examples of loan collateral are described herein forpurposes of illustration, any system benefitting from the disclosuresherein, and any considerations understood to one of skill in the arthaving the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term smart contract services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a smart contract service includesany service or application that manages a smart contract or a smartlending contract. For example, the smart contract service may specifyterms and conditions of a smart contract, such as in a rules database,or process output from a set of valuation services and assign items ofcollateral sufficient to provide security for a loan. Smart contractservices may automatically execute a set of rules or conditions thatembody the smart contract, wherein the execution may be based on or takeadvantage of collected data. Smart contract services may automaticallyinitiate a demand for payment of a loan, automatically initiate aforeclosure process, automatically initiate an action to claimsubstitute or backup collateral or transfer ownership of collateral,automatically initiate an inspection process, automatically change apayment or interest rate term that is based on the collateral, and mayalso configure smart contracts to automatically undertake a loan-relatedaction. Smart contracts may govern at least one of loan terms andconditions, loan-related events, and loan-related activities. Smartcontracts may be agreements that are encoded as computer protocols andmay facilitate, verify, or enforce the negotiation or performance of asmart contract. Smart contracts may or may not be one or more ofpartially or fully self-executing, or partially or fully self-enforcing.

Certain processes may not be considered to be smart-contract relatedindividually, but may be considered smart-contract related in anaggregated system—for example automatically undertaking a loan-relatedaction may not be smart contract-related in one instance, but in anotherinstance, may be governed by terms of a smart contract. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofprocesses systems, and any such processes or systems may be considered asmart contract or smart contract service herein, while in certainembodiments a given service may not be considered a smart contractservice herein.

One of skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system and how to combine processes andsystems from the present disclosure to implement a smart contractservice and/or enhance operations of the contemplated system. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated system includes a smart contract service or smartcontract and/or whether aspects of the present disclosure can benefit orenhance the contemplated system include, without limitation: ability totransfer ownership of collateral automatically in response to an event;automated actions available upon a finding of covenant compliance (orlack of compliance); the amenity of the collateral to clustering,re-balancing, distribution, addition, substitution, and removal of itemsfrom collateral; the modification parameters of an aspect of a loan inresponse to an event (e.g., timing, complexity, suitability for the loantype, etc.); the complexity of terms and conditions of loans for thesystem, including benefits from rapid determination and/or predictionsof changes to entities (e.g., in the collateral, a financial conditionof a party, offset collateral, and/or in an industry related to a party)related to the loan; the suitability of automated generation of termsand conditions and/or execution of terms and conditions for the types ofloans, parties, and/or industries contemplated for the system; and thelike. While specific examples of smart contract services andconsiderations are described herein for purposes of illustration, anysystem benefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term IoT system (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, an IoT system includes any systemof uniquely identified and interrelated computing devices, mechanicaland digital machines, sensors, and objects that are able to transferdata over a network without intervention. Certain components may not beconsidered an IoT system individually, but may be considered an IoTsystem in an aggregated system—for example, a single networked sensor,smart speaker, and/or medical device may be not an IoT system, but maybe a part of a larger system and/or be accumulated with a number ofother similar components to be considered an IoT system and/or a part ofan IoT system. In certain embodiments, a system may be considered an IoTsystem for some purposes but not for other purposes—for example, a smartspeaker may be considered part of an IoT system for certain operations,such as for providing surround sound, or the like, but not part of anIoT system for other operations such as directly streaming content froma single, locally networked source. Additionally, in certainembodiments, otherwise similar looking systems may be differentiated indetermining whether such systems are IoT systems, and/or which type ofIoT system. For example, one group of medical devices may not, at agiven time, be sharing to an aggregated HER database, while anothergroup of medical devices may be sharing data to an aggregate HER for thepurposes of a clinical study, and accordingly one group of medicaldevices may be an IoT system, while the other is not. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofsystems, and any such systems may be considered an IoT system herein,while in certain embodiments a given system may not be considered an IoTsystem herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, how to combineprocesses and systems from the present disclosure to enhance operationsof the contemplated system, and which circuits, controllers, and/ordevices include an IoT system for the contemplated system. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated system is an IoT system and/or whether aspects ofthe present disclosure can benefit or enhance the contemplated systeminclude, without limitation: the transmission environment of the system(e.g., availability of low power, inter-device networking); the shareddata storage of a group of devices; establishment of a geofence by agroup of devices; service as blockchain nodes; the performance of asset,collateral, or entity monitoring; the relay of data between devices;ability to aggregate data from a plurality of sensors or monitoringdevices, and the like. While specific examples of IoT systems andconsiderations are described herein for purposes of illustration, anysystem benefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term data collection services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a data collection serviceincludes any service that collects data or information, including anycircuit, controller, device, or application that may store, transmit,transfer, share, process, organize, compare, report on and/or aggregatedata. The data collection service may include data collection devices(e.g., sensors) and/or may be in communication with data collectiondevices. The data collection service may monitor entities, such as toidentify data or information for collection. The data collection servicemay be event-driven, run on a periodic basis, or retrieve data from anapplication at particular points in the application's execution. Certainprocesses may not be considered to be a data collection serviceindividually, but may be considered a data collection service in anaggregated system—for example, a networked storage device may be acomponent of a data collection service in one instance, but in anotherinstance, may have standalone functionality. Accordingly, the benefitsof the present disclosure may be applied in a wide variety of processessystems, and any such processes or systems may be considered a datacollection service herein, while in certain embodiments a given servicemay not be considered a data collection service herein. One of skill inthe art, having the benefit of the disclosure herein and knowledge abouta contemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system and how to combine processes and systems from thepresent disclosure implement a data collection service and/or to enhanceoperations of the contemplated system. Certain considerations for theperson of skill in the art, in determining whether a contemplated systemis a data collection service and/or whether aspects of the presentdisclosure can benefit or enhance the contemplated system include,without limitation: ability to modify a business rule on the fly andalter a data collection protocol; perform real-time monitoring ofevents; connection of a device for data collection to a monitoringinfrastructure, execution of computer readable instructions that cause aprocessor to log or track events; use of an automated inspection system;occurrence of sales at a networked point-of-sale; need for data from oneor more distributed sensors or cameras; and the like. While specificexamples of data collection services and considerations are describedherein for purposes of illustration, any system benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term data integration services (and similar terms) as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, a data integrationservice includes any service that integrates data or information,including any device or application that may extract, transform, load,normalize, compress, decompress, encode, decode, and otherwise processdata packets, signals, and other information. The data integrationservice may monitor entities, such as to identify data or informationfor integration. The data integration service may integrate dataregardless of required frequency, communication protocol, or businessrules needed for intricate integration patterns. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofprocesses systems, and any such processes or systems may be considered adata integration service herein, while in certain embodiments a givenservice may not be considered a data integration service herein. One ofskill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system and how to combine processes andsystems from the present disclosure to implement a data integrationservice and/or enhance operations of the contemplated system. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated system is a data integration service and/orwhether aspects of the present disclosure can benefit or enhance thecontemplated system include, without limitation: ability to modify abusiness rule on the fly and alter a data integration protocol;communication with third party databases to pull in data to integratewith; synchronization of data across disparate platforms; connection toa central data warehouse; data storage capacity, processing capacity,and/or communication capacity distributed throughout the system; theconnection of separate, automated workflows; and the like. Whilespecific examples of data integration services and considerations aredescribed herein for purposes of illustration, any system benefittingfrom the disclosures herein, and any considerations understood to one ofskill in the art having the benefit of the disclosures herein, arespecifically contemplated within the scope of the present disclosure.

The term computational services (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, computational services may beincluded as a part of one or more services, platforms, or microservices,such as blockchain services, data collection services, data integrationservices, valuation services, smart contract services, data monitoringservices, data mining, and/or any service that facilitates collection,access, processing, transformation, analysis, storage, visualization, orsharing of data. Certain processes may not be considered to be acomputational service. For example, a process may not be considered acomputational service depending on the sorts of rules governing theservice, an end product of the service, or the intent of the service.Accordingly, the benefits of the present disclosure may be applied in awide variety of processes systems, and any such processes or systems maybe considered a computational service herein, while in certainembodiments a given service may not be considered a computationalservice herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system and how to combineprocesses and systems from the present disclosure to implement one ormore computational service, and/or to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is a computationalservice and/or whether aspects of the present disclosure can benefit orenhance the contemplated system include, without limitation:agreement-based access to the service; mediate an exchange betweendifferent services; provides on demand computational power to a webservice; accomplishes one or more of monitoring, collection, access,processing, transformation, analysis, storage, integration,visualization, mining, or sharing of data. While specific examples ofcomputational services and considerations are described herein forpurposes of illustration, any system benefitting from the disclosuresherein, and any considerations understood to one of skill in the arthaving the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term sensor as utilized herein should be understood broadly. Withoutlimitation to any other aspect or description of the present disclosure,a sensor may be a device, module, machine, or subsystem that detects ormeasures a physical quality, event, or change. In embodiments, mayrecord, indicate, transmit, or otherwise respond to the detection ormeasurement. Examples of sensors may be sensors for sensing movement ofentities, for sensing temperatures, pressures or other attributes aboutentities or their environments, cameras that capture still or videoimages of entities, sensors that collect data about collateral orassets, such as, for example, regarding the location, condition (health,physical, or otherwise), quality, security, possession, or the like. Inembodiments, sensors may be sensitive to, but not influential on, theproperty to be measured but insensitive to other properties. Sensors maybe analog or digital. Sensors may include processors, transmitters,transceivers, memory, power, sensing circuit, electrochemical fluidreservoirs, light sources, and the like. Further examples of sensorscontemplated for use in the system include biosensors, chemical sensors,black silicon sensor, IR sensor, acoustic sensor, induction sensor,motion sensor, optical sensor, opacity sensor, proximity sensor,inductive sensor, Eddy-current sensor, passive infrared proximitysensor, radar, capacitance sensor, capacitive displacement sensor,hall-effect sensor, magnetic sensor, GPS sensor, thermal imaging sensor,thermocouple, thermistor, photoelectric sensor, ultrasonic sensor,infrared laser sensor, inertial motion sensor, MEMS internal motionsensor, ultrasonic 3D motion sensor, accelerometer, inclinometer, forcesensor, piezoelectric sensor, rotary encoders, linear encoders, ozonesensor, smoke sensor, heat sensor, magnetometer, carbon dioxidedetector, carbon monoxide detector, oxygen sensor, glucose sensor, smokedetector, metal detector, rain sensor, altimeter, GPS, detection ofbeing outside, detection of context, detection of activity, objectdetector (e.g. collateral), marker detector (e.g. geo-location marker),laser rangefinder, sonar, capacitance, optical response, heart ratesensor, or an RF/micropower impulse radio (MIR) sensor. In certainembodiments, a sensor may be a virtual sensor—for example determining aparameter of interest as a calculation based on other sensed parametersin the system. In certain embodiments, a sensor may be a smartsensor—for example reporting a sensed value as an abstractedcommunication (e.g., as a network communication) of the sensed value. Incertain embodiments, a sensor may provide a sensed value directly (e.g.,as a voltage level, frequency parameter, etc.) to a circuit, controller,or other device in the system. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit from a sensor. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated device is a sensor and/or whether aspects of thepresent disclosure can benefit from or be enhanced by the contemplatedsensor include, without limitation: the conditioning of anactivation/deactivation of a system to an environmental quality; theconversion of electrical output into measured quantities; the ability toenforce a geofence; the automatic modification of a loan in response tochange in collateral; and the like. While specific examples of sensorsand considerations are described herein for purposes of illustration,any system benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein, are specifically contemplated within thescope of the present disclosure.

The term storage condition and similar terms, as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, storage condition includes anenvironment, physical location, environmental quality, level ofexposure, security measures, maintenance description, accessibilitydescription, and the like related to the storage of an asset,collateral, or an entity specified and monitored in a contract, loan, oragreement or backing the contract, loan or other agreement, and thelike. Based on a storage condition of a collateral, an asset, or entity,actions may be taken to, maintain, improve, and/or confirm a conditionof the asset or the use of that asset as collateral. Based on a storagecondition, actions may be taken to alter the terms or conditions of aloan or bond. Storage condition may be classified in accordance withvarious rules, thresholds, conditional procedures, workflows, modelparameters, and the like and may be based on self-reporting or on datafrom Internet of Things devices (IoT data), data from a set ofenvironmental condition sensors, data from a set of social networkanalytic services and a set of algorithms for querying network domains,social media data, crowdsourced data, and the like. The storagecondition may be tied to a geographic location relating to thecollateral, the issuer, the borrower, the distribution of the funds orother geographic locations. Examples of IoT data may include images,sensor data, location data, and the like. Examples of social media dataor crowdsourced data may include behavior of parties to the loan,financial condition of parties, adherence to a parties to a term orcondition of the loan, or bond, or the like. Parties to the loan mayinclude issuers of a bond, related entities, lender, borrower, 3rdparties with an interest in the debt. Storage condition may relate to anasset or type of collateral such as a municipal asset, a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty. The storage condition may include an environment whereenvironment may include an environment selected from among a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle. Actions based on the storagecondition of a collateral, an asset or an entity may include managing,reporting on, altering, syndicating, consolidating, terminating,maintaining, modifying terms and/or conditions, foreclosing an asset, orotherwise handling a loan, contract, or agreement. One of skill in theart, having the benefit of the disclosure herein and knowledge about acontemplated storage condition, can readily determine which aspects ofthe present disclosure will benefit a particular application for astorage condition. Certain considerations for the person of skill in theart, or embodiments of the present disclosure in choosing an appropriatestorage condition to manage and/or monitor, include, without limitation:the legality of the condition given the jurisdiction of the transaction,the data available for a given collateral, the anticipated transactiontype (loan, bond or debt), the specific type of collateral, the ratio ofthe loan to value, the ratio of the collateral to the loan, the grosstransaction/loan amount, the credit scores of the borrower and thelender, ordinary practices in the industry, and other considerations.While specific examples of storage conditions are described herein forpurposes of illustration, any embodiment benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein are specificallycontemplated within the scope of the present disclosure.

The term geolocation and similar terms, as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, geolocation includes theidentification or estimation of the real-world geographic location of anobject, including the generation of a set of geographic coordinates(e.g. latitude and longitude) and/or street address. Based on ageolocation of a collateral, an asset, or entity, actions may be takento maintain or improve a condition of the asset or the use of that assetas collateral. Based on a geolocation, actions may be taken to alter theterms or conditions of a loan or bond. Based on a geolocation,determinations or predictions related to a transaction may beperformed—for example based upon the weather, civil unrest in aparticular area, and/or local disasters (e.g., an earthquake, flood,tornado, hurricane, industrial accident, etc.). Geolocations may bedetermined in accordance with various rules, thresholds, conditionalprocedures, workflows, model parameters, and the like and may be basedon self-reporting or on data from Internet of Things devices, data froma set of environmental condition sensors, data from a set of socialnetwork analytic services and a set of algorithms for querying networkdomains, social media data, crowdsourced data, and the like. Examples ofgeolocation data may include GPS coordinates, images, sensor data,street address, and the like. Geolocation data may be quantitative(e.g., longitude/latitude, relative to a plat map, etc.) and/orqualitative (e.g., categorical such as “coastal”, “rural”, etc.; “withinNew York City”, etc.). Geolocation data may be absolute (e.g., GPSlocation) or relative (e.g., within 100 yards of an expected location).Examples of social media data or crowdsourced data may include behaviorof parties to the loan as inferred by their geolocation, financialcondition of parties inferred by geolocation, adherence of parties to aterm or condition of the loan, or bond, or the like. Geolocation may bedetermined for an asset or type of collateral such as a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a consumable item, an edible item, a beverage, a preciousmetal, an item of jewelry, a gemstone, an antique, a fixture, an item offurniture, an item of equipment, a tool, an item of machinery, and anitem of personal property. Geolocation may be determined for an entitysuch as one of the parties, a third-party (e.g., an inspection service,maintenance service, cleaning service, etc. relevant to a transaction),or any other entity related to a transaction. The geolocation mayinclude an environment selected from among a municipal environment, acorporate environment, a securities trading environment, a real propertyenvironment, a commercial facility, a warehousing facility, atransportation environment, a manufacturing environment, a storageenvironment, a home, and a vehicle. Actions based on the geolocation ofa collateral, an asset or an entity may include managing, reporting on,altering, syndicating, consolidating, terminating, maintaining,modifying terms and/or conditions, foreclosing an asset, or otherwisehandling a loan, contract, or agreement. One of skill in the art, havingthe benefit of the disclosure herein and knowledge about a contemplatedsystem, can readily determine which aspects of the present disclosurewill benefit a particular application for a geolocation, and whichlocation aspect of an item is a geolocation for the contemplated system.Certain considerations for the person of skill in the art, orembodiments of the present disclosure in choosing an appropriategeolocation to manage, include, without limitation: the legality of thegeolocation given the jurisdiction of the transaction, the dataavailable for a given collateral, the anticipated transaction type(loan, bond or debt), the specific type of collateral, the ratio of theloan to value, the ratio of the collateral to the loan, the grosstransaction/loan amount, the frequency of travel of the borrower tocertain jurisdictions and other considerations, the mobility of thecollateral, and/or a likelihood of location-specific event occurrencerelevant to the transaction (e.g., weather, location of a relevantindustrial facility, availability of relevant services, etc.). Whilespecific examples of geolocation are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein are specifically contemplated withinthe scope of the present disclosure.

The term jurisdictional location and similar terms, as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, jurisdictional location refers tothe laws and legal authority governing a loan entity. The jurisdictionallocation may be based on a geolocation of an entity, a registrationlocation of an entity (e.g. a ship's flag state, a state ofincorporation for a business, and the like), a granting state forcertain rights such as intellectual priority, and the like. In certainembodiments, a jurisdictional location may be one or more of thegeolocations for an entity in the system. In certain embodiments, ajurisdictional location may not be the same as the geolocation of anyentity in the system (e.g., where an agreement specifies some otherjurisdiction). In certain embodiments, a jurisdictional location mayvary for entities in the system (e.g., borrower at A, lender at B,collateral positioned at C, agreement enforced at D, etc.). In certainembodiments, a jurisdictional location for a given entity may varyduring the operations of the system (e.g., due to movement ofcollateral, related data, changes in terms and conditions, etc.). Incertain embodiments, a given entity of the system may have more than onejurisdictional location (e.g., due to operations of the relevant law,and/or options available to one or more parties), and/or may havedistinct jurisdictional locations for different purposes. Ajurisdictional location of an item of collateral, an asset, or entity,actions may dictate certain terms or conditions of a loan or bond,and/or may indicate different obligations for notices to parties,foreclosure and/or default execution, treatment of collateral and/ordebt security, and/or treatment of various data within the system. Whilespecific examples of jurisdictional location are described herein forpurposes of illustration, any embodiment benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein are specificallycontemplated within the scope of the present disclosure.

The terms token of value, token, and variations such as cryptocurrencytoken, and the like, as utilized herein, in the context of increments ofvalue, may be understood broadly to describe either: (a) a unit ofcurrency or cryptocurrency (e.g. a cryptocurrency token), and (b) mayalso be used to represent a credential that can be exchanged for a good,service, data or other valuable consideration (e.g. a token of value).Without limitation to any other aspect or description of the presentdisclosure, in the former case, a token may also be used in conjunctionwith investment applications, token-trading applications, andtoken-based marketplaces. In the latter case, a token can also beassociated with rendering consideration, such as providing goods,services, fees, access to a restricted area or event, data, or othervaluable benefit. Tokens can be contingent (e.g. contingent accesstoken) or not contingent. For example, a token of value may be exchangedfor accommodations, (e.g. hotel rooms), dining/food goods and services,space (e.g. shared space, workspace, convention space, etc.),fitness/wellness goods or services, event tickets or event admissions,travel, flights or other transportation, digital content, virtual goods,license keys, or other valuable goods, services, data, or consideration.Tokens in various forms may be included where discussing a unit ofconsideration, collateral, or value, whether currency, cryptocurrency,or any other form of value such as goods, services, data or otherbenefits. One of skill in the art, having the benefit of the disclosureherein and knowledge about a token, can readily determine the valuesymbolized or represented by a token, whether currency, cryptocurrency,good, service, data, or other value. While specific examples of tokensare described herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term pricing data as utilized herein may be understood broadly todescribe a quantity of information such as a price or cost, of one ormore items in a marketplace. Without limitation to any other aspect ordescription of the present disclosure, pricing data may also be used inconjunction with spot market pricing, forward market pricing, pricingdiscount information, promotional pricing, and other informationrelating to the cost or price of items. Pricing data may satisfy one ormore conditions, or may trigger application of one or more rules of asmart contract. Pricing data may be used in conjunction with other formsof data such as market value data, accounting data, access data, assetand facility data, worker data, event data, underwriting data, claimsdata or other forms of data. Pricing data may be adjusted for thecontext of the valued item (e.g., condition, liquidity, location, etc.)and/or for the context of a particular party. One of skill in the art,having the benefit of the disclosure herein and knowledge about pricingdata, can readily determine the purposes and use of pricing data invarious embodiments and contexts disclosed herein.

Without limitation to any other aspect or description of the presentdisclosure, a token includes any token including, without limitation, atoken of value, such as collateral, an asset, a reward, such as in atoken serving as representation of value, such as a value holdingvoucher that can be exchanged for goods or services. Certain componentsmay not be considered tokens individually, but may be considered tokensin an aggregated system—for example, a value placed on an asset may notbe in itself be a token, but the value of an asset may be placed in atoken of value, such as to be stored, exchanged, traded, and the like.For instance, in a non-limiting example, a blockchain circuit may bestructured to provide lenders a mechanism to store the value of assets,where the value attributed to the token is stored in a distributedledger of the blockchain circuit, but the token itself, assigned thevalue, may be exchanged or traded such as through a token marketplace.In certain embodiments, a toke may be considered a token for somepurposes but not for other purposes—for example, a token may be used toas an indication of ownership of an asset, but this use of a token wouldnot be traded as a value where a token including the value of the assetmight. Accordingly, the benefits of the present disclosure may beapplied in a wide variety of systems, and any such systems may beconsidered a token herein, while in certain embodiments a given systemmay not be considered a token herein. One of skill in the art, havingthe benefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit a particular system,and/or how to combine processes and systems from the present disclosureto enhance operations of the contemplated system. Certain considerationsfor the person of skill in the art, in determining whether acontemplated system is a token and/or whether aspects of the presentdisclosure can benefit or enhance the contemplated system include,without limitation, access data such as relating to rights of access,tickets, and tokens; use in an investment application such as forinvestment in shares, interests, and tokens; a token-tradingapplication; a token-based marketplace; forms of consideration such asmonetary rewards and tokens; translating the value of a resources intokens; a cryptocurrency token; indications of ownership such asidentity information, event information, and token information; ablockchain-based access token traded in a marketplace application;pricing application such as for setting and monitoring pricing forcontingent access rights, underlying access rights, tokens, and fees;trading applications such as for trading or exchanging contingent accessrights or underlying access rights or tokens; tokens created and storedon a blockchain for contingent access rights resulting in an ownership(e.g., a ticket); and the like.

The term financial data as utilized herein may be understood broadly todescribe a collection of financial information about an asset,collateral or other item or items. Financial data may include revenues,expenses, assets, liabilities, equity, bond ratings, default, return onassets (ROA), return on investment (ROI), past performance, expectedfuture performance, earnings per share (EPS), internal rate of return(IRR), earnings announcements, ratios, statistical analysis of any ofthe foregoing (e.g. moving averages), and the like. Without limitationto any other aspect or description of the present disclosure, financialdata may also be used in conjunction with pricing data and market valuedata. Financial data may satisfy one or more conditions, or may triggerapplication of one or more rules of a smart contract. Financial data maybe used in conjunction with other forms of data such as market valuedata, pricing data, accounting data, access data, asset and facilitydata, worker data, event data, underwriting data, claims data or otherforms of data. One of skill in the art, having the benefit of thedisclosure herein and knowledge about financial data, can readilydetermine the purposes and use of pricing data in various embodimentsand contexts disclosed herein.

The term covenant as utilized herein may be understood broadly todescribe a term, agreement or promise, such as performance of someaction or inaction. For example, a covenant may relate to behavior of aparty or legal status of a party. Without limitation to any other aspector description of the present disclosure, a covenant may also be used inconjunction with other related terms to an agreement or loan, such as arepresentation, a warranty, an indemnity, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, aforeclose condition, a default condition, and a consequence of default.A covenant or lack of performance of a covenant may satisfy one or moreconditions, or may trigger collection, breach or other terms andconditions. In certain embodiments, a smart contract may calculatewhether a covenant is satisfied and in cases where the covenant is notsatisfied, may enable automated action or trigger other conditions orterms. One of skill in the art, having the benefit of the disclosureherein and knowledge about covenants, can readily determine the purposesand use of covenants in various embodiments and contexts disclosedherein.

The term entity as utilized herein may be understood broadly to describea party, a third-party (e.g., an auditor, regulator, service provider,etc.), and/or an identifiable related object such as an item ofcollateral related to a transaction. Example entities include anindividual, partnership, corporation, limited liability company or otherlegal organization. Other example entities include an identifiable itemof collateral, offset collateral, potential collateral, or the like. Forexample, an entity may be a given party, such as an individual, to anagreement or loan. Data or other terms herein may be characterized ashaving a context relating to an entity, such as entity-oriented data. Anentity may be characterized with a specific context or application, suchas a human entity, physical entity, transactional entity or a financialentity, without limitation. An entity may have representatives thatrepresent or act on its behalf. Without limitation to any other aspector description of the present disclosure, an entity may also be used inconjunction with other related entities or terms to an agreement orloan, such as a representation, a warranty, an indemnity, a covenant, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a party, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a foreclose condition, a defaultcondition, and a consequence of default. An entity may have a set ofattributes such as: a publicly stated valuation, a set of property ownedby the entity as indicated by public records, a valuation of a set ofproperty owned by the entity, a bankruptcy condition, a foreclosurestatus, a contractual default status, a regulatory violation status, acriminal status, an export controls status, an embargo status, a tariffstatus, a tax status, a credit report, a credit rating, a web siterating, a set of customer reviews for a product of an entity, a socialnetwork rating, a set of credentials, a set of referrals, a set oftestimonials, a set of behavior, a location, and a geolocation, withoutlimitation. In certain embodiments, a smart contract may calculatewhether an entity has satisfied conditions or covenants and in caseswhere the entity has not satisfied such conditions or covenants, mayenable automated action or trigger other conditions or terms. One ofskill in the art, having the benefit of the disclosure herein andknowledge about entities, can readily determine the purposes and use ofentities in various embodiments and contexts disclosed herein.

The term party as utilized herein may be understood broadly to describea member of an agreement, such as an individual, partnership,corporation, limited liability company or other legal organization. Forexample, a party may be a primary lender, a secondary lender, a lendingsyndicate, a corporate lender, a government lender, a bank lender, asecured lender, a bond issuer, a bond purchaser, an unsecured lender, aguarantor, a provider of security, a borrower, a debtor, an underwriter,an inspector, an assessor, an auditor, a valuation professional, agovernment official, an accountant or other entities having rights orobligations to an agreement, transaction or loan. A party maycharacterize a different term, such as transaction as in the termmulti-party transaction, where multiple parties are involved in atransaction, or the like, without limitation. A party may haverepresentatives that represent or act on its behalf. In certainembodiments, the term party may reference a potential party or aprospective party—for example, an intended lender or borrowerinteracting with a system, that may not yet be committed to an actualagreement during the interactions with the system. Without limitation toany other aspect or description of the present disclosure, an party mayalso be used in conjunction with other related parties or terms to anagreement or loan, such as a representation, a warranty, an indemnity, acovenant, a balance of debt, a fixed interest rate, a variable interestrate, a payment amount, a payment schedule, a balloon payment schedule,a specification of collateral, a specification of substitutability ofcollateral, an entity, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a foreclose condition, a defaultcondition, and a consequence of default. A party may have a set ofattributes such as: an identity, a creditworthiness, an activity, abehavior, a business practice, a status of performance of a contract,information about accounts receivable, information about accountspayable, information about the value of collateral, and other types ofinformation, without limitation. In certain embodiments, a smartcontract may calculate whether a party has satisfied conditions orcovenants and in cases where the party has not satisfied such conditionsor covenants, may enable automated action or trigger other conditions orterms. One of skill in the art, having the benefit of the disclosureherein and knowledge about parties, can readily determine the purposesand use of parties in various embodiments and contexts disclosed herein.

The term party attribute, entity attribute, or party/entity attribute asutilized herein may be understood broadly to describe a value,characteristic, or status of a party or entity. For example, attributesof a party or entity may be, without limitation: value, quality,location, net worth, price, physical condition, health condition,security, safety, ownership, identity, creditworthiness, activity,behavior, business practice, status of performance of a contract,information about accounts receivable, information about accountspayable, information about the value of collateral, and other types ofinformation, and the like. In certain embodiments, a smart contract maycalculate values, status or conditions associated with attributes of aparty or entity, and in cases where the party or entity has notsatisfied such conditions or covenants, may enable automated action ortrigger other conditions or terms. One of skill in the art, having thebenefit of the disclosure herein and knowledge about attributes of aparty or entity, can readily determine the purposes and use of theseattributes in various embodiments and contexts disclosed herein.

The term lender as utilized herein may be understood broadly to describea party to an agreement offering an asset for lending, proceeds of aloan, and may include an individual, partnership, corporation, limitedliability company, or other legal organization. For example, a lendermay be a primary lender, a secondary lender, a lending syndicate, acorporate lender, a government lender, a bank lender, a secured lender,an unsecured lender, or other party having rights or obligations to anagreement, transaction or loan offering a loan to a borrower, withoutlimitation. A lender may have representatives that represent or act onits behalf. Without limitation to any other aspect or description of thepresent disclosure, a party may also be used in conjunction with otherrelated parties or terms to an agreement or loan, such as a borrower, aguarantor, a representation, a warranty, an indemnity, a covenant, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a security, a personal guarantee, a lien, a duration, aforeclose condition, a default condition, and a consequence of default.In certain embodiments, a smart contract may calculate whether a lenderhas satisfied conditions or covenants and in cases where the lender hasnot satisfied such conditions or covenants, may enable automated action,a notification or alert, or trigger other conditions or terms. One ofskill in the art, having the benefit of the disclosure herein andknowledge about a lender, can readily determine the purposes and use ofa lender in various embodiments and contexts disclosed herein.

The term crowdsourcing services as utilized herein may be understoodbroadly to describe services offered or rendered in conjunction with acrowdsourcing model or transaction, wherein a large group of people orentities supply contributions to fulfill a need, such as a loan, for thetransaction. Crowdsourcing services may be provided by a platform orsystem, without limitation. A crowdsourcing request may be communicatedto a group of information suppliers and by which responses to therequest may be collected and processed to provide a reward to at leastone successful information supplier. The request and parameters may beconfigured to obtain information related to the condition of a set ofcollateral for a loan. The crowdsourcing request may be published. Incertain embodiments, without limitation, crowdsourcing services may beperformed by a smart contract, wherein the reward is managed by a smartcontract that processes responses to the crowdsourcing request andautomatically allocates a reward to information that satisfies a set ofparameter configured for the crowdsourcing request. One of skill in theart, having the benefit of the disclosure herein and knowledge aboutcrowdsourcing services, can readily determine the purposes and use ofcrowdsourcing services in various embodiments and contexts disclosedherein.

The term publishing services as utilized herein may be understood todescribe a set of services to publish a crowdsourcing request.Publishing services may be provided by a platform or system, withoutlimitation. In certain embodiments, without limitation, publishingservices may be performed by a smart contract, wherein the crowdsourcingrequest is published or publication is initiated by the smart contract.One of skill in the art, having the benefit of the disclosure herein andknowledge about publishing services, can readily determine the purposesand use of publishing services in various embodiments and contextsdisclosed herein.

The term interface as utilized herein may be understood broadly todescribe a component by which interaction or communication is achieved,such as a component of a computer, which may be embodied in software,hardware or a combination thereof. For example, an interface may serve anumber of different purposes or be configured for different applicationsor contexts, such as, without limitation: an application programminginterface, a graphic user interface, user interface, software interface,marketplace interface, demand aggregation interface, crowdsourcinginterface, secure access control interface, network interface, dataintegration interface or a cloud computing interface, or combinationsthereof. An interface may serve to act as a way to enter, receive ordisplay data, within the scope of lending, refinancing, collection,consolidation, factoring, brokering or foreclosure, without limitation.An interface may serve as an interface for another interface. Withoutlimitation to any other aspect or description of the present disclosure,an interface may be used in conjunction with applications, processes,modules, services, layers, devices, components, machines, products,sub-systems, interfaces, connections, or as part of a system. In certainembodiments, an interface may be embodied in software, hardware or acombination thereof, as well as stored on a medium or in memory. One ofskill in the art, having the benefit of the disclosure herein andknowledge about an interface, can readily determine the purposes and useof an interface in various embodiments and contexts disclosed herein.

The term graphical user interface as utilized herein may be understoodas a type of interface to allow a user to interact with a system,computer or other interface, in which interaction or communication isachieved through graphical devices or representations. A graphical userinterface may be a component of a computer, which may be embodied incomputer readable instructions, hardware, or a combination thereof. Agraphical user interface may serve a number of different purposes or beconfigured for different applications or contexts. Such an interface mayserve to act as a way to receive or display data using visualrepresentation, stimulus or interactive data, without limitation. Agraphical user interface may serve as an interface for another graphicaluser interface or other interface. Without limitation to any otheraspect or description of the present disclosure, a graphical userinterface may be used in conjunction with applications, processes,modules, services, layers, devices, components, machines, products,sub-systems, interfaces, connections, or as part of a system. In certainembodiments, a graphical user interface may be embodied in computerreadable instructions, hardware or a combination thereof, as well asstored on a medium or in memory. Graphical user interfaces may beconfigured for any input types, including keyboards, a mouse, a touchscreen, and the like. Graphical user interfaces may be configured forany desired user interaction environments, including for example adedicated application, a web page interface, or combinations of these.One of skill in the art, having the benefit of the disclosure herein andknowledge about a graphical user interface, can readily determine thepurposes and use of a graphical user interface in various embodimentsand contexts disclosed herein.

The term user interface as utilized herein may be understood as a typeof interface to allow a user to interact with a system, computer, orother apparatus, in which interaction or communication is achievedthrough graphical devices or representations. A user interface may be acomponent of a computer, which may be embodied in software, hardware, ora combination thereof. The user interface may be stored on a medium orin memory. User interfaces may include drop-down menus, tables, forms,or the like with default, templated, recommended, or pre-configuredconditions. In certain embodiments, a user interface may include voiceinteraction. Without limitation to any other aspect or description ofthe present disclosure, a user interface may be used in conjunction withapplications, circuits, controllers, processes, modules, services,layers, devices, components, machines, products, sub-systems,interfaces, connections, or as part of a system. User interfaces mayserve a number of different purposes or be configured for differentapplications or contexts. For example, a lender-side user interface mayinclude features to view a plurality of customer profiles, but may berestricted from making certain changes. A debtor-side user interface mayinclude features to view details and make changes to a user account. A3rd party neutral-side interface (e.g. a 3^(rd) party not having aninterest in an underlying transaction, such as a regulator, auditor,etc.) may have features that enable a view of company oversight andanonymized user data without the ability to manipulate any data, and mayhave scheduled access depending upon the 3^(rd) party and the purposefor the access. A 3rd party interested-side interface (e.g. a 3^(rd)party that may have an interest in an underlying transaction, such as acollector, debtor advocate, investigator, partial owner, etc.) mayinclude features enabling a view of particular user data withrestrictions on making changes. Many more features of these userinterfaces may be available to implement embodiments of the systemsand/or procedures described throughout the present disclosure.Accordingly, the benefits of the present disclosure may be applied in awide variety of processes and systems, and any such processes or systemsmay be considered a service herein. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a user interface,can readily determine the purposes and use of a user interface invarious embodiments and contexts disclosed herein. Certainconsiderations for the person of skill in the art, in determiningwhether a contemplated interface is a user interface and/or whetheraspects of the present disclosure can benefit or enhance thecontemplated system include, without limitation: configurable views,ability to restrict manipulation or views, report functions, ability tomanipulate user profile and data, implement regulatory requirements,provide the desired user features for borrowers, lenders, and 3^(rd)parties, and the like.

Interfaces and dashboards as utilized herein may further be understoodbroadly to describe a component by which interaction or communication isachieved, such as a component of a computer, which may be embodied insoftware, hardware, or a combination thereof. Interfaces and dashboardsmay acquire, receive, present, or otherwise administrate an item,service, offering or other aspect of a transaction or loan. For example,interfaces and dashboards may serve a number of different purposes or beconfigured for different applications or contexts, such as, withoutlimitation: an application programming interface, a graphic userinterface, user interface, software interface, marketplace interface,demand aggregation interface, crowdsourcing interface, secure accesscontrol interface, network interface, data integration interface or acloud computing interface, or combinations thereof. An interface ordashboard may serve to act as a way to receive or display data, withinthe context of lending, refinancing, collection, consolidation,factoring, brokering or foreclosure, without limitation. An interface ordashboard may serve as an interface or dashboard for another interfaceor dashboard. Without limitation to any other aspect or description ofthe present disclosure, an interface may be used in conjunction withapplications, circuits, controllers, processes, modules, services,layers, devices, components, machines, products, sub-systems,interfaces, connections, or as part of a system. In certain embodiments,an interface or dashboard may be embodied in computer readableinstructions, hardware, or a combination thereof, as well as stored on amedium or in memory. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the purposes and use ofinterfaces and/or dashboards in various embodiments and contextsdisclosed herein.

The term domain as utilized herein may be understood broadly to describea scope or context of a transaction and/or communications related to atransaction. For example, a domain may serve a number of differentpurposes or be configured for different applications or contexts, suchas, without limitation: a domain for execution, a domain for a digitalasset, domains to which a request will be published, domains to whichsocial network data collection and monitoring services will be applied,domains to which Internet of Things data collection and monitoringservices will be applied, network domains, geolocation domains,jurisdictional location domains, and time domains. Without limitation toany other aspect or description of the present disclosure, one or moredomains may be utilized relative to any applications, circuits,controllers, processes, modules, services, layers, devices, components,machines, products, sub-systems, interfaces, connections, or as part ofa system. In certain embodiments, a domain may be embodied in computerreadable instructions, hardware, or a combination thereof, as well asstored on a medium or in memory. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a domain, canreadily determine the purposes and use of a domain in variousembodiments and contexts disclosed herein.

The term request (and variations) as utilized herein may be understoodbroadly to describe the action or instance of initiating or asking for athing (e.g. information, a response, an object, and the like) to beprovided. A specific type of request may also serve a number ofdifferent purposes or be configured for different applications orcontexts, such as, without limitation: a formal legal request (e.g. asubpoena), a request to refinance (e.g. a loan), or a crowdsourcingrequest. Systems may be utilized to perform requests as well as fulfillrequests. Requests in various forms may be included where discussing alegal action, a refinancing of a loan, or a crowdsourcing service,without limitation. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system, can readilydetermine the value of a request implemented in an embodiment. Whilespecific examples of requests are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term reward (and variations) as utilized herein may be understoodbroadly to describe a thing or consideration received or provided inresponse to an action or stimulus. Rewards can be of a financial type,or non-financial type, without limitation. A specific type of reward mayalso serve a number of different purposes or be configured for differentapplications or contexts, such as, without limitation: a reward event,claims for rewards, monetary rewards, rewards captured as a data set,rewards points, and other forms of rewards. Rewards may be triggered,allocated, generated for innovation, provided for the submission ofevidence, requested, offered, selected, administrated, managed,configured, allocated, conveyed, identified, without limitation, as wellas other actions. Systems may be utilized to perform the aforementionedactions. Rewards in various forms may be included where discussing aparticular behavior, or encouragement of a particular behavior, withoutlimitation. In certain embodiments herein, a reward may be utilized as aspecific incentive (e.g., rewarding a particular person that responds toa crowdsourcing request) or as a general incentive (e.g., providing areward responsive to a successful crowdsourcing request, in addition toor alternatively to a reward to the particular person that responded).One of skill in the art, having the benefit of the disclosure herein andknowledge about a reward, can readily determine the value of a rewardimplemented in an embodiment. While specific examples of rewards aredescribed herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term robotic process automation system as utilized herein may beunderstood broadly to describe a system capable of performing tasks orproviding needs for a system of the present disclosure. For example, arobotic process automation system, without limitation, can be configuredfor: negotiation of a set of terms and conditions for a loan,negotiation of refinancing of a loan, loan collection, consolidating aset of loans, managing a factoring loan, brokering a mortgage loan,training for foreclosure negotiations, configuring a crowdsourcingrequest based on a set of attributes for a loan, setting a reward,determining a set of domains to which a request will be published,configuring the content of a request, configuring a data collection andmonitoring action based on a set of attributes of a loan, determining aset of domains to which the Internet of Things data collection andmonitoring services will be applied, and iteratively training andimproving based on a set of outcomes. A robotic process automationsystem may include: a set of data collection and monitoring services, anartificial intelligence system, and another robotic process automationsystem which is a component of the higher level robotic processautomation system. The robotic process automation system may include: atleast one of the set of mortgage loan activities and the set of mortgageloan interactions includes activities among marketing activity,identification of a set of prospective borrowers, identification ofproperty, identification of collateral, qualification of borrower, titlesearch, title verification, property assessment, property inspection,property valuation, income verification, borrower demographic analysis,identification of capital providers, determination of available interestrates, determination of available payment terms and conditions, analysisof existing mortgage, comparative analysis of existing and new mortgageterms, completion of application workflow, population of fields ofapplication, preparation of mortgage agreement, completion of scheduleto mortgage agreement, negotiation of mortgage terms and conditions withcapital provider, negotiation of mortgage terms and conditions withborrower, transfer of title, placement of lien and closing of mortgageagreement. Example and non-limiting robotic process automation systemsmay include one or more user interfaces, interfaces with circuits and/orcontrollers throughout the system to provide, request, and/or sharedata, and/or one or more artificial intelligence circuits configured toiteratively improve one or more operations of the robotic processautomation system. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated robotic process automation system, can readily determinethe circuits, controllers, and/or devices to include to implement arobotic process automation system performing the selected functions forthe contemplated system. While specific examples of robotic processautomation systems are described herein for purposes of illustration,any embodiment benefitting from the disclosures herein, and anyconsiderations understood.

The term loan-related action (and other related terms such asloan-related event and loan-related activity) are utilized herein andmay be understood broadly to describe one or multiple actions, events oractivities relating to a transaction that includes a loan within thetransaction. The action, event or activity may occur in many differentcontexts of loans, such as lending, refinancing, consolidation,factoring, brokering, foreclosure, administration, negotiating,collecting, procuring, enforcing, and data processing (e.g. datacollection), or combinations thereof, without limitation. A loan-relatedaction may be used in the form of a noun (e.g. a notice of default hasbeen communicated to the borrower with formal notice, which could beconsidered a loan-related action). A loan-related action, event, oractivity may refer to a single instance, or may characterize a group ofactions, events, or activities. For example, a single action such asproviding a specific notice to a borrower of an overdue payment may beconsidered a loan-related action. Similarly, a group of actions fromstart to finish relating to a default may also be considered a singleloan-related action. Appraisal, inspection, funding and recording,without limitation, may all also be considered loan-related actions thathave occurred, as well as events relating to the loan, and may also beloan-related events. Similarly, these activities of completing theseactions may also be considered loan-related activities (e.g. appraising,inspecting, funding, recording, etc.), without limitation. In certainembodiments, a smart contract or robotic process automation system mayperform loan-related actions, loan-related events, or loan-relatedactivities for one or more of the parties, and process appropriate tasksfor completion of the same. In some cases the smart contract or roboticprocess automation system may not complete a loan-related action, anddepending upon such outcome this may enable an automated action or maytrigger other conditions or terms. One of skill in the art, having thebenefit of the disclosure herein and knowledge about loan-relatedactions, events, and activities can readily determine the purposes anduse of this term in various forms and embodiments as describedthroughout the present disclosure.

The term loan-related action, events, and activities, as noted herein,may also more specifically be utilized to describe a context for callingof a loan. A calling of a loan is an action wherein the lender candemand the loan be repaid, usually triggered by some other condition orterm, such as delinquent payment(s). For example, a loan-related actionfor calling of the loan may occur when a borrower misses three paymentsin a row, such that there is a severe delinquency in the loan paymentschedule, and the loan goes into default. In such a scenario, a lendermay be initiating loan-related actions for calling of the loan toprotect its rights. In such a scenario, perhaps the borrower pays a sumto cure the delinquency and penalties, which may also be considered as aloan-related action for calling of the loan. In some circumstances asmart contract or robotic process automation system may initiate,administrate, or process loan-related actions for calling of the loan,which without limitation, may including providing notice, researching,and collecting payment history, or other tasks performed as a part ofthe calling of the loan. One of skill in the art, having the benefit ofthe disclosure herein and knowledge about loan-related actions forcalling of the loan, or other forms of the term and its various forms,can readily determine the purposes and use of this term in the contextof an event or other various embodiments and contexts disclosed herein.

The term loan-related action, events, and activities, as noted herein,may also more specifically be utilized to describe a context for paymentof a loan. Typically in transactions involving loans, withoutlimitation, a loan is repaid on a payment schedule. Various actions maybe taken to provide a borrower with information to pay back the loan, aswell as actions for a lender to receive payment for the loan. Forexample, if a borrower makes a payment on the loan, a loan-relatedaction for payment of the loan may occur. Without limitation, such apayment may comprise several actions that may occur with respect to thepayment on the loan, such as: the payment being tendered to the lender,the loan ledger or accounting reflecting that a payment has been made, areceipt provided to the borrower of the payment made, and the nextpayment being requested of the borrower. In some circumstances a smartcontract or robotic process automation system may initiate,administrate, or process such loan-related actions for payment of theloan, which without limitation, may including providing notice to thelender, researching, and collecting payment history, providing a receiptto the borrower, providing notice of the next payment due to theborrower, or other actions associated with payment of the loan. One ofskill in the art, having the benefit of the disclosure herein andknowledge about loan-related actions for payment of a loan, or otherforms of the term and its various forms, can readily determine thepurposes and use of this term in the context of an event or othervarious embodiments and contexts disclosed herein.

The term loan-related action, events, and activities, as noted herein,may also more specifically be utilized to describe a context for apayment schedule or alternative payment schedule. Typically intransactions involving loans, without limitation, a loan is repaid on apayment schedule, which may be modified over time. Or, such a paymentschedule may be developed and agreed in the alternative, with analternative payment schedule. Various actions may be taken in thecontext of a payment schedule or alternate payment schedule for thelender or the borrower, such as: the amount of such payments, when suchpayments are due, what penalties or fees may attach to late payments, orother terms. For example, if a borrower makes an early payment on theloan, a loan-related action for payment schedule and alternative paymentschedule of the loan may occur; in such case, perhaps the payment isapplied as principal, with the regular payment still being due. Withoutlimitation, loan-related actions for a payment schedule and alternativepayment schedule may comprise several actions that may occur withrespect to the payment on the loan, such as: the payment being tenderedto the lender, the loan ledger or accounting reflecting that a paymenthas been made, a receipt provided to the borrower of the payment made, acalculation if any fees are attached or due, and the next payment beingrequested of the borrower. In certain embodiments, an activity todetermine a payment schedule or alternative payment schedule may be aloan-related action, event, or activity. In certain embodiments, anactivity to communicate the payment schedule or alternative paymentschedule (e.g., to the borrower, the lender, or a 3rd party) may be aloan-related action, event, or activity. In some circumstances a smartcontract circuit or robotic process automation system may initiate,administrate, or process such loan-related actions for payment scheduleand alternative payment schedule, which without limitation, may includeproviding notice to the lender, researching and collecting paymenthistory, providing a receipt to the borrower, calculating the next duedate, calculating the final payment amount and date, providing notice ofthe next payment due to the borrower, determining the payment scheduleor an alternate payment schedule, communicating the payment scheduler oran alternate payment schedule, or other actions associated with paymentof the loan. One of skill in the art, having the benefit of thedisclosure herein and knowledge about loan-related actions for paymentschedule and alternative payment schedule, or other forms of the termand its various forms, can readily determine the purposes and use ofthis term in the context of an event or other various embodiments andcontexts disclosed herein.

The term regulatory notice requirement (and any derivatives) as utilizedherein may be understood broadly to describe an obligation or conditionto communicate a notification or message to another party or entity. Theregulatory notice requirement may be required under one or moreconditions that are triggered, or generally required. For example, alender may have a regulatory notice requirement to provide notice to aborrower of a default of a loan, or change of an interest rate of aloan, or other notifications relating to a transaction or loan. Theregulatory aspect of the term may be attributed to jurisdiction-specificlaws, rules, or codes that require certain obligations of communication.In certain embodiments, a policy directive may be treated as aregulatory notice requirement—for example where a lender has an internalnotice policy that may exceed the regulatory requirements of one or moreof the jurisdictional locations related to a transaction. The noticeaspect generally relates to formal communications, which may take manydifferent forms, but may specifically be specified as a particular formof notice, such as a certified mail, facsimile, email transmission, orother physical or electronic form, a content for the notice, and/or atiming requirement related to the notice. The requirement aspect relatesto the necessity of a party to complete its obligation to be incompliance with laws, rules, codes, policies, standard practices, orterms of an agreement or loan. In certain embodiments, a smart contractmay process or trigger regulatory notice requirements and provideappropriate notice to a borrower. This may be based on location of atleast one of: the lender, the borrower, the funds provided via the loan,the repayment of the loan, and the collateral of the loan, or otherlocations as designated by the terms of the loan, transaction, oragreement. In cases where a party or entity has not satisfied suchregulatory notice requirements, certain changes in the rights orobligations between the parties may be triggered—for example where alender provides a non-compliant notice to the borrower, an automatedaction or trigger based on the terms and conditions of the loan, and/orbased on external information (e.g., a regulatory prescription, internalpolicy of the lender, etc.) may be effected by a smart contract circuitand/or robotic process automation system may be implemented. One ofskill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system, can readilydetermine the purposes and use of regulatory notice requirements invarious embodiments and contexts disclosed herein.

The term regulatory notice requirement may also be utilized herein todescribe an obligation or condition to communicate a notification ormessage to another party or entity based upon a general or specificpolicy, rather than based on a particular jurisdiction, or laws, rules,or codes of a particular location (as in regulatory notice requirementthat may be jurisdiction-specific). The regulatory notice requirementmay be prudent or suggested, rather than obligatory or required, underone or more conditions that are triggered, or generally required. Forexample, a lender may have a regulatory notice requirement that ispolicy based to provide notice to a borrower of a new informationalwebsite, or will experience a change of an interest rate of a loan inthe future, or other notifications relating to a transaction or loanthat are advisory or helpful, rather than mandatory (although mandatorynotices may also fall under a policy basis). Thus, in policy based usesof the regulatory notice requirement term, a smart contract circuit mayprocess or trigger regulatory notice requirements and provideappropriate notice to a borrower which may or may not necessarily berequired by a law, rule, or code. The basis of the notice orcommunication may be out of prudence, courtesy, custom, or obligation.

The term regulatory notice may also be utilized herein to describe anobligation or condition to communicate a notification or message toanother party or entity specifically, such as a lender or borrower. Theregulatory notice may be specifically directed toward any party orentity, or a group of parties or entities. For example, a particularnotice or communication may be advisable or required to be provided to aborrower, such as on circumstances of a borrower's failure to providescheduled payments on a loan resulting in a default. As such, such aregulatory notice directed to a particular user, such as a lender orborrower, may be as a result of a regulatory notice requirement that isjurisdiction-specific or policy-based, or otherwise. Thus, in somecircumstances a smart contract may process or trigger a regulatorynotice and provide appropriate notice to a specific party such as aborrower, which may or may not necessarily be required by a law, rule,or code, but may otherwise be provided out of prudence, courtesy orcustom. In cases where a party or entity has not satisfied suchregulatory notice requirements to a specific party or parties, it maycreate circumstances where certain rights may be forgiven by one or moreparties or entities, or may enable automated action or trigger otherconditions or terms. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the purposes and use ofregulatory notice requirements based in various embodiments and contextsdisclosed herein.

The term regulatory foreclosure requirement (and any derivatives) asutilized herein may be understood broadly to describe an obligation orcondition in order to trigger, process or complete default of a loan,foreclosure or recapture of collateral, or other related foreclosureactions. The regulatory foreclosure requirement may be required underone or more conditions that are triggered, or generally required. Forexample, a lender may have a regulatory foreclosure requirement toprovide notice to a borrower of a default of a loan, or othernotifications relating to the default of a loan prior to foreclosure.The regulatory aspect of the term may be attributed tojurisdiction-specific laws, rules, or codes, that require certainobligations of communication. The foreclosure aspect generally relatesto the specific remedy of foreclosure, or a recapture of collateralproperty and default of a loan, which may take many different forms, butmay be specified in the terms of the loan. The requirement aspectrelates to the necessity of a party to complete its obligation in orderto be in compliance or performance of laws, rules, codes, or terms of anagreement or loan. In certain embodiments, a smart contract circuit mayprocess or trigger regulatory foreclosure requirements and processappropriate tasks relating to such a foreclosure action. Foreclosureaction(s) may be based on a jurisdictional location of at least one ofthe lender, the borrower, the fund provided via the loan, the repaymentof the loan, and the collateral of the loan, or other locations asdesignated by the terms of the loan, transaction, or agreement. In caseswhere a party or entity has not satisfied such regulatory foreclosurerequirements, certain rights may be forgiven by the party or entity(e.g. a lender), or such a failure to comply with the regulatory noticerequirement may enable automated action or trigger other conditions orterms. One of skill in the art, having the benefit of the disclosureherein and knowledge ordinarily available about a contemplated system,can readily determine the purposes and use of regulatory foreclosurerequirements in various embodiments and contexts disclosed herein.

The term regulatory foreclosure requirement may also be utilized hereinto describe an obligation or to trigger, process, or complete default ofa loan, foreclosure or recapture of collateral, or other relatedforeclosure actions, based upon a general or specific policy rather thanbased on a particular jurisdiction, or laws, rules, or codes of aparticular location (as in regulatory foreclosure requirement that maybe jurisdiction-specific). The regulatory foreclosure requirement may beprudent or suggested, rather than obligatory or required, under one ormore conditions that are triggered, or generally required. For example,a lender may have a regulatory foreclosure requirement that is policybased to provide notice to a borrower of a default of a loan, or othernotifications relating to a transaction or loan that are advisory orhelpful, rather than mandatory (although mandatory notices may also fallunder a policy basis). Thus, in policy based uses of the regulatoryforeclosure requirement term, a smart contract may process or triggerregulatory foreclosure requirements and provide appropriate notice to aborrower which may or may not necessarily be required by a law, rule, orcode. The basis of the notice or communication may be out of prudence,courtesy, custom, industry practice, or obligation.

The term regulatory foreclosure requirements may also be utilized hereinto describe an obligation or condition that is to be performed withregard to a specific user, such as a lender or a borrower. Theregulatory notice may be specifically directed toward any party orentity, or a group of parties or entities. For example, a particularnotice or communication may be advisable or required to be provided to aborrower, such as on circumstances of a borrower's failure to providescheduled payments on a loan resulting in a default. As such, such aregulatory foreclosure requirement is directed to a particular user,such as a lender or borrower, and may be a result of a regulatoryforeclosure requirement that is jurisdiction-specific or policy-based,or otherwise. For example, the foreclosure requirement may be related toa specific entity involved with a transaction (e.g., the currentborrower has been a customer for 30 years, so s/he receives uniquetreatment), or to a class of entities (e.g., “preferred” borrowers, or“first time default” borrowers). Thus, in some circumstances a smartcontract circuit may process or trigger an obligation or action thatmust be taken pursuant to a foreclosure, where the action is directed orfrom a specific party such as a lender or a borrower, which may or maynot necessarily be required by a law, rule, or code, but may otherwisebe provided out of prudence, courtesy, or custom. In certainembodiments, the obligation or condition that is to be performed withregard to the specific user may form a part of the terms and conditionsor otherwise be known to the specific user to which it applies (e.g., aninsurance company or bank that advertises a specific practice withregard to a specific class of customers, such as first-time defaultcustomers, first-time accident customers, etc.), and in certainembodiments the obligation or condition that is to be performed withregard to the specific user may be unknown to the specific user to whichit applies (e.g., a bank has a policy relating to a class of users towhich the specific user belongs, but the specific user is not aware ofthe classification).

The terms value, valuation, and valuation model (and similar terms) asutilized herein should be understood broadly to describe an approach toevaluate and determine the estimated value for collateral. Withoutlimitation to any other aspect or description of the present disclosure,a valuation model may be used in conjunction with: collateral (e.g. asecured property), artificial intelligence services (e.g. to improve avaluation model), data collection and monitoring services (e.g. to set avaluation amount), valuation services (e.g. the process of informing,using, and/or improving a valuation model), and/or outcomes relating totransactions in collateral (e.g. as a basis of improving the valuationmodel). “Jurisdiction-specific valuation model” is also used as avaluation model used in a specific geographic/jurisdictional area orregion; wherein, the jurisdiction can be specific to jurisdiction of thelender, the borrower, the delivery of funds, the payment of the loan orthe collateral of the loan, or combinations thereof. In certainembodiments, a jurisdiction-specific valuation model considersjurisdictional effects on a valuation of collateral, including at least:rights and obligations for borrowers and lenders in the relevantjurisdiction(s); jurisdictional effects on the ability to move, import,export, substitute, and/or liquidate the collateral; jurisdictionaleffects on the timing between default and foreclosure or collection ofcollateral; and/or jurisdictional effects on the volatility and/orsensitivity of collateral value determinations. In certain embodiments,a geolocation-specific valuation model considers geolocation effects ona valuation of the collateral, which may include a similar list ofconsiderations of relative jurisdictional effects (although thejurisdictional location(s) may be distinct from the geolocation(s)), butmay also include additional effects, such as: weather-related effects;distance of the collateral from monitoring, maintenance, or seizureservices; and/or proximity of risk phenomenon (e.g., fault lines,industrial locations, a nuclear plant, etc.). A valuation model mayutilize a valuation of offset collateral (e.g., a similar item ofcollateral, a generic value such as a market value of similar orfungible collateral, and/or a value of an item that correlates with avalue of the collateral) as a part of the valuation of the collateral.In certain embodiments, an artificial intelligence circuit includes oneor more machine learning and/or artificial intelligence algorithms, toimprove a valuation model, including, for example, utilizing informationover time between multiple transactions involving similar or offsetcollateral, and/or utilizing outcome information (e.g., where loantransactions are completed successfully or unsuccessfully, and/or inresponse to collateral seizure or liquidation events that demonstratereal-world collateral valuation determinations) from the same or othertransactions to iteratively improve the valuation model. In certainembodiments, an artificial intelligence circuit is trained on acollateral valuation data set, for example previously determinedvaluations and/or through interactions with a trainer (e.g., a human,accounting valuations, and/or other valuation data). In certainembodiments, the valuation model and/or parameters of the valuationmodel (e.g., assumptions, calibration values, etc.) may be determinedand/or negotiated as a part of the terms and conditions of thetransaction (e.g., a loan, a set of loans, and/or a subset of the set ofloans). One of skill in the art, having the benefit of the disclosureherein and knowledge ordinarily available about a contemplated system,can readily determine which aspects of the present disclosure willbenefit a particular application for a valuation model, and how tochoose or combine valuation models to implement an embodiment of avaluation model. Certain considerations for the person of skill in theart, or embodiments of the present disclosure in choosing an appropriatevaluation model, include, without limitation: the legal considerationsof a valuation model given the jurisdiction of the collateral; the dataavailable for a given collateral; the anticipated transaction/loantype(s); the specific type of collateral; the ratio of the loan tovalue; the ratio of the collateral to the loan; the grosstransaction/loan amount; the credit scores of the borrower; accountingpractices for the loan type and/or related industry; uncertaintiesrelated to any of the foregoing; and/or sensitivities related to any ofthe foregoing. While specific examples of valuation models andconsiderations are described herein for purposes of illustration, anyembodiment benefitting from the disclosures herein, and anyconsiderations understood to one of skill in the art having the benefitof the disclosures herein, are specifically contemplated within thescope of the present disclosure.

The term market value data, or marketplace information, (and other formsor variations) as utilized herein may be understood broadly to describedata or information relating to the valuation of a property, asset,collateral or other valuable item which may be used as the subject of aloan, or transaction. Market value data or marketplace information maychange from time to time, and may be estimated, calculated, orobjectively or subjectively determined from various sources ofinformation. Market value data or marketplace information may be relateddirectly to an item of collateral or to an offset item of collateral.Market value data or marketplace information may include financial data,market ratings, product ratings, customer data, market research tounderstand customer needs or preferences, competitive intelligenceregarding competitors, suppliers, and the like, entities sales,transactions, customer acquisition cost, customer lifetime value, brandawareness, churn rate, and the like. The term may occur in manydifferent contexts of contracts or loans, such as lending, refinancing,consolidation, factoring, brokering, foreclosure, and data processing(e.g. data collection), or combinations thereof, without limitation.Market value data or marketplace information may be used as a noun toidentify a single figure or a plurality of figures or data. For example,market value data or marketplace information may be utilized by a lenderto determine if a property or asset will serve as collateral for asecured loan, or may alternatively be utilized in the determination offoreclosure if a loan is in default, without limitation to thesecircumstances in use of the term. Marketplace value data or marketplaceinformation may also be used to determine loan-to-value figures orcalculations. In certain embodiments, a collection service, smartcontract circuit, and/or robotic process automation system may estimateor calculate market value data or marketplace information from one ormore sources of data or information. In some cases market data value ormarketplace information, depending upon the data/information containedtherein, may enable automated action or trigger other conditions orterms. One of skill in the art, having the benefit of the disclosureherein and knowledge ordinarily available about a contemplated systemand available relevant marketplace information, can readily determinethe purposes and use of this term in various forms, embodiments andcontexts disclosed herein.

The terms similar collateral, similar to collateral, offset collateral,and other forms or variations as utilized herein may be understoodbroadly to describe a property, asset, or valuable item that may be likein nature to a collateral (e.g. an article of value held in security)regarding a loan or other transaction. Similar collateral may refer to aproperty, asset, collateral, or other valuable item which may beaggregated, substituted, or otherwise referred to in conjunction withother collateral, whether the similarity comes in the form of a commonattribute such as type of item of collateral, category of the item ofcollateral, an age of the item of collateral, a condition of the item ofcollateral, a history of the item of collateral, an ownership of theitem of collateral, a caretaker of the item of collateral, a security ofthe item of collateral, a condition of an owner of the item ofcollateral, a lien on the item of collateral, a storage condition of theitem of collateral, a geolocation of the item of collateral, and ajurisdictional location of the item of collateral, and the like. Incertain embodiments, an offset collateral references an item that has avalue correlation with an item of collateral—for example, an offsetcollateral may exhibit similar price movements, volatility, storagerequirements, or the like for an item of collateral. In certainembodiments, similar collateral may be aggregated to form a largersecurity interest or collateral for an additional loan or distribution,or transaction. In certain embodiments, offset collateral may beutilized to inform a valuation of the collateral. In certainembodiments, a smart contract circuit or robotic process automationsystem may estimate or calculate figures, data or information relatingto similar collateral, or may perform a function with respect toaggregating similar collateral. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system can readily determine the purposes and useof similar collateral, offset collateral, or related terms as theyrelate to collateral in various forms, embodiments, and contextsdisclosed herein.

The term restructure (and other forms such as restructuring) as utilizedherein may be understood broadly to describe a modification of terms orconditions, properties, collateral, or other considerations affecting aloan or transaction. Restructuring may result in a successful outcomewhere amended terms or conditions are adopted between parties, or anunsuccessful outcome where no modification or restructure occurs,without limitation. Restructuring can occur in many contexts ofcontracts or loans, such as application, lending, refinancing,collection, consolidation, factoring, brokering, foreclosure, andcombinations thereof, without limitation. Debt may also be restructured,which may indicate that debts owed to a party are modified as to timing,amounts, collateral, or other terms. For example, a borrower mayrestructure debt of a loan to accommodate a change of financialconditions, or a lender may offer to a borrower the restructuring of adebt for its own needs or prudence. In certain embodiments, a smartcontract circuit or robotic process automation system may automaticallyor manually restructure debt based on a monitored condition, or createoptions for restructuring a debt, administrate the process ofnegotiating or effecting the restructuring of a debt, or other actionsin connection with restructuring or modifying terms of a loan ortransaction. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the purposes and use of thisterm, whether in the context of debt or otherwise, in variousembodiments and contexts disclosed herein.

The term social network data collection, social network monitoringservices, and social network data collection and monitoring services(and its various forms or derivatives) as utilized herein may beunderstood broadly to describe services relating to the acquisition,organizing, observing, or otherwise acting upon data or informationderived from one or more social networks. The social network datacollection and monitoring services may be a part of a related system ofservices or a standalone set of services. Social network data collectionand monitoring services may be provided by a platform or system, withoutlimitation. Social network data collection and monitoring services maybe used in a variety of contexts such as lending, refinancing,negotiation, collection, consolidation, factoring, brokering,foreclosure, and combinations thereof, without limitation. Requests ofsocial network data collection and monitoring, with configurationparameters, may be requested by other services, automatically initiated,or automatically triggered to occur based on conditions or circumstancesthat occur. An interface may be provided to configure, initiate,display, or otherwise interact with social network data collection andmonitoring services. Social networks, as utilized herein, reference anymass platform where data and communications occur between individualsand/or entities, where the data and communications are at leastpartially accessible to an embodiment system. In certain embodiments,the social network data includes publicly available (e.g., accessiblewithout any authorization) information. In certain embodiments, thesocial network data includes information that is properly accessible toan embodiment system, but may include subscription access or otheraccess to information that is not freely available to the public, butmay be accessible (e.g., consistent with a privacy policy of the socialnetwork with its users). A social network may be primarily social innature, but may additionally or alternatively include professionalnetworks, alumni networks, industry related networks, academicallyoriented networks, or the like. In certain embodiments, a social networkmay be a crowdsourcing platform, such as a platform configured to acceptqueries or requests directed to users (and/or a subset of users,potentially meeting specified criteria), where users may be aware thatcertain communications will be shared and accessible to requestors, atleast a portion of users of the platform, and/or publicly available. Incertain embodiments, without limitation, social network data collectionand monitoring services may be performed by a smart contract circuit ora robotic process automation system. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system, can readily determine the purposes and useof social network data collection and monitoring services in variousembodiments and contexts disclosed herein.

The term crowdsource and social network information as utilized hereinmay further be understood broadly to describe information acquired orprovided in conjunction with a crowdsourcing model or transaction, orinformation acquired or provided on or in conjunction with a socialnetwork. Crowdsource and social network information may be provided by aplatform or system, without limitation. Crowdsource and social networkinformation may be acquired, provided, or communicated to or from agroup of information suppliers and by which responses to the request maybe collected and processed. Crowdsource and social network informationmay provide information, conditions, or factors relating to a loan oragreement. Crowdsource and social network information may be private orpublished, or combinations thereof, without limitation. In certainembodiments, without limitation, crowdsource and social networkinformation may be acquired, provided, organized, or processed, withoutlimitation, by a smart contract circuit, wherein the crowdsource andsocial network information may be managed by a smart contract circuitthat processes the information to satisfy a set of configuredparameters. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system can readily determine the purposes and use of thisterm in various embodiments and contexts disclosed herein.

The term negotiate (and other forms such as negotiating or negotiation)as utilized herein may be understood broadly to describe discussions orcommunications to bring about or obtain a compromise, outcome, oragreement between parties or entities. Negotiation may result in asuccessful outcome where terms are agreed between parties, or anunsuccessful outcome where the parties do not agree to specific terms,or combinations thereof, without limitation. A negotiation may besuccessful in one aspect or for a particular purpose, and unsuccessfulin another aspect or for another purpose. Negotiation can occur in manycontexts of contracts or loans, such as lending, refinancing,collection, consolidation, factoring, brokering, foreclosure, andcombinations thereof, without limitation. For example, a borrower maynegotiate an interest rate or loan terms with a lender. In anotherexample, a borrower in default may negotiate an alternative resolutionto avoid foreclosure with a lender. In certain embodiments, a smartcontract circuit or robotic process automation system may negotiate forone or more of the parties, and process appropriate tasks for completingor attempting to complete a negotiation of terms. In some casesnegotiation by the smart contract or robotic process automation systemmay not complete or be successful. Successful negotiation may enableautomated action or trigger other conditions or terms to be implementedby the smart contract circuit or robotic process automation system. Oneof skill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system, can readilydetermine the purposes and use of negotiation in various embodiments andcontexts disclosed herein.

The term negotiate in various forms may more specifically be utilizedherein in verb form (e.g. to negotiate) or in noun forms (e.g. anegotiation), or other forms to describe a context of mutual discussionleading to an outcome. For example, a robotic process automation systemmay negotiate terms and conditions on behalf of a party, which would bea use as a verb clause. In another example, a robotic process automationsystem may be negotiating terms and conditions for modification of aloan, or negotiating a consolidation offer, or other terms. As a nounclause, a negotiation (e.g. an event) may be performed by a roboticprocess automation system. Thus, in some circumstances a smart contractcircuit or robotic process automation system may negotiate (e.g. as averb clause) terms and conditions, or the description of doing so may beconsidered a negotiation (e.g. as a noun clause). One of skill in theart, having the benefit of the disclosure herein and knowledge aboutnegotiating and negotiation, or other forms of the word negotiate, canreadily determine the purposes and use of this term in variousembodiments and contexts disclosed herein.

The term negotiate in various forms may also specifically be utilized todescribe an outcome, such as a mutual compromise or completion ofnegotiation leading to an outcome. For example, a loan may, by roboticprocess automation system or otherwise, be considered negotiated as asuccessful outcome that has resulted in an agreement between parties,where the negotiation has reached completion. Thus, in somecircumstances a smart contract circuit or robotic process automationsystem may have negotiated to completion a set of terms and conditions,or a negotiated loan. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available for a contemplatedsystem, can readily determine the purposes and use of this term as itrelates to a mutually agreed outcome through completion of negotiationin various embodiments and contexts disclosed herein.

The term negotiate in various forms may also specifically be utilized tocharacterize an event such as a negotiating event, or an eventnegotiation, including reaching a set of agreeable terms betweenparties. An event requiring mutual agreement or compromise betweenparties may be considered a negotiating event, without limitation. Forexample, during the procurement of a loan, the process of reaching amutually acceptable set of terms and conditions between parties could beconsidered a negotiating event. Thus, in some circumstances a smartcontract circuit or robotic process automation system may accommodatethe communications, actions, or behaviors of the parties for anegotiated event.

The term collection (and other forms such as collect or collecting) asutilized herein may be understood broadly to describe the acquisition ofa tangible (e.g. physical item), intangible (e.g. data, a license, or aright), or monetary (e.g. payment) item, or other obligation or assetfrom a source. The term generally may relate to the entire prospectiveacquisition of such an item from related tasks in early stages torelated tasks in late stages or full completion of the acquisition ofthe item. Collection may result in a successful outcome where the itemis tendered to a party, or may or an unsuccessful outcome where the itemis not tendered or acquired to a party, or combinations thereof (e.g., alate or otherwise deficient tender of the item), without limitation.Collection may occur in many different contexts of contracts or loans,such as lending, refinancing, consolidation, factoring, brokering,foreclosure, and data processing (e.g. data collection), or combinationsthereof, without limitation. Collection may be used in the form of anoun (e.g. data collection or the collection of an overdue payment whereit refers to an event or characterizes an event), may refer as a noun toan assortment of items (e.g. a collection of collateral for a loan whereit refers to a number of items in a transaction), or may be used in theform of a verb (e.g. collecting a payment from the borrower). Forexample, a lender may collect an overdue payment from a borrower throughan online payment, or may have a successful collection of overduepayments acquired through a customer service telephone call. In certainembodiments, a smart contract circuit or robotic process automationsystem may perform collection for one or more of the parties, andprocess appropriate tasks for completing or attempting collection forone or more items (e.g. an overdue payment). In some cases negotiationby the smart contract or robotic process automation system may notcomplete or be successful, and depending upon such outcomes this mayenable automated action or trigger other conditions or terms. One ofskill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system, can readilydetermine the purposes and use of collection in various forms,embodiments, and contexts disclosed herein.

The term collection in various forms may also more specifically beutilized herein in noun form to describe a context for an event orthing, such as a collection event, or a collection payment. For example,a collection event may refer to a communication to a party or otheractivity that relates to acquisition of an item in such an activity,without limitation. A collection payment, for example, may relate to apayment made by a borrower that has been acquired through the process ofcollection, or through a collection department with a lender. Althoughnot limited to an overdue, delinquent, or defaulted loan, collection maycharacterize an event, payment or department, or other noun associatedwith a transaction or loan, as being a remedy for something that hasbecome overdue. Thus, in some circumstances a smart contract circuit orrobotic process automation system may collect a payment or installmentfrom a borrower, and the activity of doing so may be considered acollection event, without limitation.

The term collection in various forms may also more specifically beutilized herein as an adjective or other forms to describe a contextrelating to litigation, such as the outcome of a collection litigation(e.g. litigation regarding overdue or default payments on a loan). Forexample, the outcome of a collection litigation may be related todelinquent payments which are owed by a borrower or other party, andcollection efforts relating to those delinquent payments may belitigated by parties. Thus, in some circumstances a smart contractcircuit or robotic process automation system may receive, determine, orotherwise administrate the outcome of collection litigation.

The term collection in various forms may also more specifically beutilized herein as an adjective or other forms to describe a contextrelating to an action of acquisition, such as a collection action (e.g.actions to induce tendering or acquisition of overdue or defaultpayments on a loan or other obligation). The terms collection yield,financial yield of collection, and/or collection financial yield may beused. The result of such a collection action may or may not have afinancial yield. For example, a collection action may result in thepayment of one or more outstanding payments on a loan, which may rendera financial yield to another party such as the lender. Thus, in somecircumstances a smart contract circuit or robotic process automationsystem may render a financial yield from a collection action, orotherwise administrate or in some manner assist in a financial yield ofa collection action. In embodiments, a collection action may include theneed for collection litigation.

The term collection in various forms (collection ROI, ROI on collection,ROI on collection activity, collection activity ROI, and the like) mayalso more specifically be utilized herein to describe a context relatingto an action of receiving value, such as a collection action (e.g.actions to induce tendering or acquisition of overdue or defaultpayments on a loan or other obligation), wherein there is a return oninvestment (ROI). The result of such a collection action may or may nothave an ROI, either with respect to the collection action itself (as anROI on the collection action) or as an ROI on the broader loan ortransaction that is the subject of the collection action. For example,an ROI on a collection action may be prudent or not with respect to adefault loan, without limitation, depending upon whether the ROI will beprovided to a party such as the lender. A projected ROI on collectionmay be estimated, or may also be calculated given real events thattranspire. In some circumstances a smart contract circuit or roboticprocess automation system may render an estimated ROI for a collectionaction or collection event, or may calculate an ROI for actual eventstranspiring in a collection action or collection event, withoutlimitation. In embodiments, such a ROI may be a positive or negativefigure, whether estimated or actual.

The term reputation, measure of reputation, lender reputation, borrowerreputation, entity reputation, and the like may include general, widelyheld beliefs, opinions, and/or perceptions that are generally held aboutan individual, entity, collateral, and the like. A measure forreputation may be determined based on social data includinglikes/dislikes, review of entity or products and services provided bythe entity, rankings of the company or product, current and historicmarket and financial data include price, forecast, buy/sellrecommendations, financial news regarding entity, competitors, andpartners. Reputations may be cumulative in that a product reputation andthe reputation of a company leader or lead scientist may influence theoverall reputation of the entity. Reputation of an institute associatedwith an entity (e.g. a school being attended by a student) may influencethe reputation of the entity. In some circumstances a smart contractcircuit or robotic process automation system may collect or initiatecollection of data related to the above and determine a measure orranking of reputation. A measure or ranking of an entity's reputationmay be used by a smart contract circuit or robotic process automationsystem in determining whether to enter into an agreement with theentity, determination of terms and conditions of a loan, interest rates,and the like. In certain embodiments, indicia of a reputationdetermination may be related to outcomes of one or more transactions(e.g., a comparison of “likes” on a particular social media data set toan outcome index, such as successful payments, successful negotiationoutcomes, ability to liquidate a particular type of collateral, etc.) todetermine the measure or ranking of an entity's reputation. One of skillin the art, having the benefit of the disclosure herein and knowledgeordinarily available about a contemplated system, can readily determinethe purposes and use of the reputation, a measure or ranking of thereputation, and/or utilization of the reputation in negotiations,determination of terms and conditions, determination of whether toproceed with a transaction, and other various embodiments and contextsdisclosed herein.

The term collection in various forms (e.g. collector) may also morespecifically be utilized herein to describe a party or entity thatinduces, administrates, or facilitates a collection action, collectionevent, or other collection related context. The measure of reputation ofa party involved, such as a collector, or during the context of acollection, may be estimated or calculated using objective, subjective,or historical metrics or data. For example, a collector may be involvedin a collection action, and the reputation of that collector may be usedto determine decisions, actions or conditions. Similarly, a collectionmay be also used to describe objective, subjective, or historicalmetrics or data to measure the reputation of a party involved, such as alender, borrower, or debtor. In some circumstances a smart contractcircuit or robotic process automation system may render a collection ormeasures, or implement a collector, within the context of a transactionor loan.

The term collection and data collection in various forms, including datacollection systems, may also more specifically be utilized herein todescribe a context relating to the acquisition, organization, orprocessing of data, or combinations thereof, without limitation. Theresult of such a data collection may be related or wholly unrelated to acollection of items (e.g., grouping of the items, either physically orlogically), or actions taken for delinquent payments (e.g., collectionof collateral, a debt, or the like), without limitation. For example, adata collection may be performed by a data collection system, whereindata is acquired, organized, or processed for decision-making,monitoring, or other purposes of prospective or actual transaction orloan. In some circumstances a smart contract or robotic processautomation system may incorporate data collection or a data collectionsystem, to perform portions or entire tasks of data collection, withoutlimitation. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available for a contemplatedsystem, can readily determine and distinguish the purposes and use ofcollection in the context of data or information as used herein.

The terms refinance, refinancing activity(ies), refinancinginteractions, refinancing outcomes, and similar terms, as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure refinance andrefinancing activities include replacing an existing mortgage, loan,bond, debt transaction, or the like with a new mortgage, loan, bond, ordebt transaction that pays off or ends the previous financialarrangement. In certain embodiments, any change to terms and conditionsof a loan, and/or any material change to terms and conditions of a loan,may be considered a refinancing activity. In certain embodiments, arefinancing activity is considered only those changes to a loanagreement that result in a different financial outcome for the loanagreement. Typically, the new loan should be advantageous to theborrower or issuer, and/or mutually agreeable (e.g., improving a rawfinancial outcome of one, and a security or other outcome for theother). Refinancing may be done to reduce interest rates, lower regularpayments, change the loan term, change the collateral associated withthe loan, consolidate debt into a single loan, restructure debt, changea type of loan (e.g. variable rate to fixed rate), pay off a loan thatis due, in response to an improved credit score, to enlarge the loan,and/or in response to a change in market conditions (e.g. interestrates, value of collateral, and the like).

Refinancing activity may include initiating an offer to refinance,initiating a request to refinance, configuring a refinancing interestrate, configuring a refinancing payment schedule, configuring arefinancing balance in a response to the amount or terms of therefinanced loan, configuring collateral for a refinancing includingchanges in collateral used, changes in terms and conditions for thecollateral, a change in the amount of collateral and the like, managinguse of proceeds of a refinancing, removing or placing a lien ondifferent items of collateral as appropriate given changes in terms andconditions as part of a refinancing, verifying title for a new orexisting item of collateral to be used to secure the refinanced loan,managing an inspection process title for a new or existing item ofcollateral to be used to secure the refinanced loan, populating anapplication to refinance a loan, negotiating terms and conditions for arefinanced loan and closing a refinancing. Refinance and refinancingactivities may be disclosed in the context of data collection andmonitoring services that collect a training set of interactions betweenentities for a set of loan refinancing activities. Refinance andrefinancing activities may be disclosed in the context of an artificialintelligence system that is trained using the collected training set ofinteractions that includes both refinancing activities and outcomes. Thetrained artificial intelligence may then be used to recommend arefinance activity, evaluate a refinance activity, make a predictionaround an expected outcome of refinancing activity, and the like.Refinance and refinancing activities may be disclosed in the context ofsmart contract systems which may automate a subset of the interactionsand activities of refinancing. In an example, a smart contract systemmay automatically adjust an interest rate for a loan based oninformation collected via at least one of an Internet of Things system,a crowdsourcing system 120, a set of social network analytic servicesand a set of data collection and monitoring services. The interest ratemay be adjusted based on rules, thresholds, model parameters thatdetermine, or recommend, an interest rate for refinancing a loan basedon interest rates available to the lender from secondary lenders, riskfactors of the borrower (including predicted risk based on one or morepredictive models using artificial intelligence), marketing factors(such as competing interest rates offered by other lenders), and thelike. Outcomes and events of a refinancing activity may be recorded in adistributed ledger. Based on the outcome of a refinance activity, asmart contract for the refinance loan may be automatically reconfiguredto define the terms and conditions for the new loan such as a principalamount of debt, a balance of debt, a fixed interest rate, a variableinterest rate, a payment amount, a payment schedule, a balloon paymentschedule, a specification of collateral, a specification ofsubstitutability of collateral, a party, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, and a consequence of default.

One of skill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system can readilydetermine which aspects of the present disclosure will benefit from aparticular application of a refinance activity, how to choose or combinerefinance activities, how to implement systems, services, or circuits toautomatically perform of one or more (or all) aspects of a refinanceactivity, and the like. Certain considerations for the person of skillin the art, for embodiments of the present disclosure in choosing anappropriate training sets of interactions with which to train anartificial intelligence to take action, recommend or predict the outcomeof certain refinance activities. While specific examples of refinanceand refinancing activities are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms consolidate, consolidation activity(ies), loan consolidation,debt consolidation, consolidation plan, and similar terms, as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, consolidate,consolidation activity(ies), loan consolidation, debt consolidation, orconsolidation plan are related to the use of a single large loan to payoff several smaller loans, and/or the use of one or more of a set ofloans to pay off at least a portion of one or more of a second set ofloans. In embodiments, loan consolidation may be secured (i.e. backed bycollateral) or unsecured. Loans may be consolidated to obtain a lowerinterest rate than one or more of the current loans, to reduce totalmonthly loan payments, and/or to bring a debtor into compliance on theconsolidated loans or other debt obligations of the debtor. Loans thatmay be classified as candidates for consolidation may be determinedbased on a model that processes attributes of entities involved in theset of loans including identity of a party, interest rate, paymentbalance, payment tem1s, payment schedule, type of loan, type ofcollateral, financial condition of party, payment status, condition ofcollateral, and value of collateral. Consolidation activities mayinclude managing at least one of identification of loans from a set ofcandidate loans, preparation of a consolidation offer, preparation of aconsolidation plan, preparation of content communicating a consolidationoffer, scheduling a consolidation offer, communicating a consolidationoffer, negotiating a modification of a consolidation offer, preparing aconsolidation agreement, executing a consolidation agreement, modifyingcollateral for a set of loans, handling an application workflow forconsolidation, managing an inspection, managing an assessment, settingan interest rate, deferring a payment requirement, setting a paymentschedule, and closing a consolidation agreement. In embodiments, theremay be systems, circuits, and/or services configured to create,configure (such as using one or more templates or libraries), modify,set, or otherwise handle (such as in a user interface) various rules,thresholds, conditional procedures, workflows, model parameters, and thelike to determine, or recommend, a consolidation action or plan for alending transaction or a set of loans based on one or more events,conditions, states, actions, or the like. In embodiments, aconsolidation plan may be based on various factors, such as the statusof payments, interest rates of the set of loans, prevailing interestrates in a platform marketplace or external marketplace, the status ofthe borrowers of a set of loans, the status of collateral or assets,risk factors of the borrower, the lender, one or more guarantors, marketrisk factors and the like. Consolidation and consolidation activitiesmay be disclosed in the context of data collection and monitoringservices that collect a training set of interactions between entitiesfor a set of loan consolidation activities. consolidation andconsolidation activities may be disclosed in the context of anartificial intelligence system that is trained using the collectedtraining set of interactions that includes both consolidation activitiesand outcomes associated with those activities. The trained artificialintelligence may then be used to recommend a consolidation activity,evaluate a consolidation activity, make a prediction around an expectedoutcome of consolidation activity, and the like based models includingstatus of debt, condition of collateral or assets used to secure or backa set of loans, the state of a business or business operation (e.g.,receivables, payables, or the like), conditions of parties (such as networth, wealth, debt, location, and other conditions), behaviors ofparties (such as behaviors indicating preferences, behaviors indicatingdebt preferences), and others. Debt consolidation, loan consolidationand associated consolidation activities may be disclosed in the contextof smart contract systems which may automate a subset of theinteractions and activities of consolidation. In embodiments,consolidation may include consolidation with respect to terms andconditions of sets of loans, selection of appropriate loans,configuration of payment terms for consolidated loans, configuration ofpayoff plans for pre-existing loans, communications to encourageconsolidation, and the like. In embodiments the artificial intelligenceof a smart contract may automatically recommend or set rules,thresholds, actions, parameters and the like (optionally by learning todo so based on a training set of outcomes over time), resulting in arecommended consolidation plan, which may specify a series of actionsrequired to accomplish a recommended or desired outcome of consolidation(such as within a range of acceptable outcomes), which may be automatedand may involve conditional execution of steps based on monitoredconditions and/or smart contract terms, which may be created,configured, and/or accounted for by the consolidation plan.Consolidation plans may be determined and executed based at least onepart on market factors (such as competing interest rates offered byother lenders, values of collateral, and the like) as well as regulatoryand/or compliance factors. Consolidation plans may be generated and/orexecuted for creation of new consolidated loans, for secondary loansrelated to consolidated loans, for modifications of existing loansrelated to consolidation, for refinancing terms of a consolidated loan,for foreclosure situations (e.g., changing from secured loan rates tounsecured loan rates), for bankruptcy or insolvency situations, forsituations involving market changes (e.g., changes in prevailinginterest rates) and others. consolidation.

Certain of the activities related to loans, collateral, entities, andthe like, may apply to a wide variety of loans and may not applyexplicitly to consolidation activities. The categorization of theactivities as consolidation activities may be based on the context ofthe loan for which the activities are taking place. However, one ofskill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system can readilydetermine which aspects of the present disclosure will benefit from aparticular application of a consolidation activity, how to choose orcombine consolidation activities, how to implement selected services,circuits, and/or systems described herein to perform certain loanconsolidation operations, and the like. While specific examples ofconsolidation and consolidation activities are described herein forpurposes of illustration, any embodiment benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The terms factoring a loan, factoring a loan transaction, factors,factoring a loan interaction, factoring assets or sets of assets usedfor factoring and similar terms, as utilized herein should be understoodbroadly. Without limitation to any other aspect or description of thepresent disclosure factoring may be applied to factoring assets such asinvoices, inventory, accounts receivable, and the like, where therealized value of the item is in the future. For example, the accountsreceivable is worth more when it has been paid and there is less risk ofdefault. Inventory and Work in Progress (WIP) may be worth more as finalproduct rather than components. References to accounts receivable shouldbe understood to encompass these terms and not be limiting. Factoringmay include a sale of accounts receivable at a discounted rate for valuein the present (often cash). Factoring may also include the use ofaccounts receivable as collateral for a short term loan. In both casesthe value of the accounts receivable or invoices may be discounted formultiple reasons including the future value of money, a term of theaccounts receivable (e.g., 30 day net payment vs. 90 day net payment), adegree of default risk on the accounts receivable, a status ofreceivables, a status of work-in-progress (WIP), a status of inventory,a status of delivery and/or shipment, financial condition(s) of partiesowing against the accounts receivable, a status of shipped and/orbilled, a status of payments, a status of the borrower, a status ofinventory, a risk factor of a borrower, a lender, one or moreguarantors, market risk factors, a status of debt (are there other lienspresent on the accounts receivable or payment owed on the inventory, acondition of collateral assets (e.g. the condition of the inventory—isit current or out of date, are invoices in arrears), a state of abusiness or business operation, a condition of a party to thetransaction (such as net worth, wealth, debt, location, and otherconditions), a behavior of a party to the transaction (such as behaviorsindicating preferences, behaviors indicating negotiation styles, and thelike), current interest rates, any current regulatory and complianceissues associated with the inventory or accounts receivable (e.g. ifinventory is being factored, has the intended product receivedappropriate approvals), and there legal actions against the borrower,and many others, including predicted risk based on one or morepredictive models using artificial intelligence). A factor is anindividual, business, entity, or groups thereof which agree to providevalue inf exchange for either the outright acquisition of the invoicesin a sale or the use of the invoices as collateral for a loan for thevalue. Factoring a loan may include the identification of candidates(both lenders and borrowers) for factoring, a plan for factoringspecifying the proposed receivables (e.g. all, some, only those meetingcertain criteria), and a proposed discount factor, communication of theplan to potential parties, proffering an offer and receiving an offer,verification of quality of receivables, conditions regarding treatmentof the receivables for the term of the loan. While specific examples offactoring and factoring activities are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms mortgage, brokering a mortgage, mortgage collateral, mortgageloan activities, and/or mortgage related activities as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a mortgage is an interactionwhere a borrower provides the title or a lien on the title of an item ofvalue, typically property, to a lender as security in exchange for moneyor another item of value, to be repaid, typically with interest, to thelender. The exchange includes the condition that, upon repayment of theloan, the title reverts to the borrower and/or the lien on the propertyis removed. The brokering of a mortgage may include the identificationof potential properties, lenders, and other parties to the loan, andarranging or negotiating the terms of the mortgage. Certain componentsor activities may not be considered mortgage related individually, butmay be considered mortgage related when used in conjunction with amortgage, act upon a mortgage, are related to an entity or party to amortgage, and the like. For example, brokering may apply to the offeringof a variety of loans including unsecured loans, outright sale ofproperty and the like. Mortgage activities and mortgage interactions mayinclude mortgage marketing activity, identification of a set ofprospective borrowers, identification of property to mortgage,identification of collateral property to mortgage, qualification ofborrower, title search and/or title verification for prospectivemortgage property, property assessment, property inspection, or propertyvaluation for prospective mortgage property, income verification,borrower demographic analysis, identification of capital providers,determination of available interest rates, determination of availablepayment terms and conditions, analysis of existing mortgage(s),comparative analysis of existing and new mortgage terms, completion ofapplication workflow (e.g. keep the application moving forward byinitiating next steps in the process as appropriate), population offields of application, preparation of mortgage agreement, completion ofschedule for mortgage agreement, negotiation of mortgage terms andconditions with capital provider, negotiation of mortgage terms andconditions with borrower, transfer of title, placement of lien onmortgaged property and closing of mortgage agreement, and similar terms,as utilized herein should be understood broadly. While specific examplesof mortgages and mortgage brokering are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms debt management, debt transactions, debt actions, debt termsand conditions, syndicating debt, consolidating debt, and/or debtportfolios, as utilized herein should be understood broadly. Withoutlimitation to any other aspect or description of the present disclosurea debt includes something of monetary value that is owed to another. Aloan typically results in the borrower holding the debt (e.g. the moneythat must be paid back according to the terms of the loan, which mayinclude interest). Consolidation of debt includes the use of a new,single loan to pay back multiple loans (or various other configurationsof debt structuring as described herein, and as understood to one ofskill in the art). Often the new loan may have better terms or lowerinterest rates. Debt portfolios include a number of pieces or groups ofdebt, often having different characteristics including term, risk, andthe like. Debt portfolio management may involve decisions regarding thequantity and quality of the debt being held and how best to balance thevarious debts to achieve a desired risk/reward position based on:investment policy, return on risk determinations for individual piecesof debt, or groups of debt. Debt may be syndicated where multiplelenders fund a single loan (or set of loans) to a borrower. Debtportfolios may be sold to a third party (e.g., at a discounted rate).Debt compliance includes the various measures taken to ensure that debtis repaid. Demonstrating compliance may include documentation of theactions taken to repay the debt.

Transactions related to a debt (debt transactions) and actions relatedto the debt (debt actions) may include offering a debt transaction,underwriting a debt transaction, setting an interest rate, deferring apayment requirement, modifying an interest rate, validating title,managing inspection, recording a change in title, assessing the value ofan asset, calling a loan, closing a transaction, setting terms andconditions for a transaction, providing notices required to be provided,foreclosing on a set of assets, modifying terms and conditions, settinga rating for an entity, syndicating debt, and/or consolidating debt.Debt terms and conditions may include a balance of debt, a principalamount of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of assets that back the bond, a specification ofsubstitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default. While specific examples of debt management anddebt management activities are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms condition, condition classification, classification models,condition management, and similar terms, as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure condition, conditionclassification, classification models, condition management, includeclassifying or determining a condition of an asset, issuer, borrower,loan, debt, bond, regulatory status, term or condition for a bond, loanor debt transaction that is specified and monitored in the contract, andthe like. Based on a classified condition of an asset, conditionmanagement may include actions to maintain or improve a condition of theasset or the use of that asset as collateral. Based on a classifiedcondition of an issuer, borrower, party regulatory status, and the like,condition management may include actions to alter the terms orconditions of a loan or bond. Condition classification may includevarious rules, thresholds, conditional procedures, workflows, modelparameters, and the like to classify a condition of an asset, issuer,borrower, loan, debt, bond, regulatory status, term or condition for abond, loan or debt transaction, and the like based on data from Internetof Things devices, data from a set of environmental condition sensors,data from a set of social network analytic services and a set ofalgorithms for querying network domains, social media data, crowdsourceddata, and the like. Condition classification may include grouping orlabeling entities, or clustering the entities, as similarly positionedwith regard to some aspect of the classified condition (e.g., a risk,quality, ROI, likelihood for recovery, likelihood to default, or someother aspect of the related debt).

Various classification models are disclosed where the classification andclassification model may be tied to a geographic location relating tothe collateral, the issuer, the borrower, the distribution of the fundsor other geographic locations. Classification and classification modelsare disclosed where artificial intelligence is used to improve aclassification model (e.g. refine a model by making refinements usingartificial intelligence data). Thus artificial intelligence may beconsidered, in some instances, as a part of a classification model andvice versa. Classification and classification models are disclosed wheresocial media data, crowdsourced data, or IoT data is used as input forrefining a model, or as input to a classification model. Examples of IoTdata may include images, sensor data, location data, and the like.Examples of social media data or crowdsourced data may include behaviorof parties to the loan, financial condition of parties, adherence to aparties to a term or condition of the loan, or bond, or the like.Parties to the loan may include issuers of a bond, related entities,lender, borrower, 3rd parties with an interest in the debt. Conditionmanagement may be discussed in connection with smart contract serviceswhich may include condition classification, data collection andmonitoring, and bond, loan and debt transaction management. Datacollection and monitoring services are also discussed in conjunctionwith classification and classification models which are related whenclassifying an issuer of a bond issuer, an asset or collateral assetrelated to the bond, collateral assets backing the bond, parties to thebond, and sets of the same. In some embodiments a classification modelmay be included when discussing bond types. Specific steps, factors orrefinements may be considered a part of a classification model. Invarious embodiments, the classification model may change both in anembodiment, or in the same embodiment which is tied to a specificjurisdiction. Different classification models may use different datasets (e.g. based on the issuer, the borrower, the collateral assets, thebond type, the loan type, and the like) and multiple classificationmodels may be used in a single classification. For example, one type ofbond, such as a municipal bond, may allow a classification model that isbased on bond data from municipalities of similar size and economicprosperity, whereas another classification model may emphasize data fromIoT sensors associated with a collateral asset. Accordingly, differentclassification models will offer benefits or risks over otherclassification models, depending upon the embodiment and the specificsof the bond, loan or debt transaction. A classification model includesan approach or concept for classification. Conditions classified for abond, loan, or debt transaction may include a principal amount of debt,a balance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of assets that back the bond, loan or debt transaction, aspecification of substitutability of assets, a party, an issuer, apurchaser, a guarantee, a guarantor, a security, a personal guarantee, alien, a duration, a covenant, a foreclose condition, a defaultcondition, and/or a consequence of default. Conditions classified mayinclude type of bond issuer such as a municipality, a corporation, acontractor, a government entity, a non-governmental entity, and anon-profit entity. Entities may include a set of issuers, a set ofbonds, a set of parties, and/or a set of assets. Conditions classifiedmay include an entity condition such as net worth, wealth, debt,location, and other conditions), behaviors of parties (such as behaviorsindicating preferences, behaviors indicating debt preferences), and thelike. Conditions classified may include an asset or type of collateralsuch as a municipal asset, a vehicle, a ship, a plane, a building, ahome, real estate property, undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property. Conditions classified mayinclude a bond type where bond type may include a municipal bond, agovernment bond, a treasury bond, an asset-backed bond, and a corporatebond. Conditions classified may include a default condition, aforeclosure condition, a condition indicating violation of a covenant, afinancial risk condition, a behavioral risk condition, a policy riskcondition, a financial health condition, a physical defect condition, aphysical health condition, an entity risk condition and an entity healthcondition. Conditions classified may include an environment whereenvironment may include an environment selected from among a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle. Actions based on thecondition of an asset, issuer, borrower, loan, debt, bond, regulatorystatus and the like, may include managing, reporting on, syndicating,consolidating, or otherwise handling a set of bonds (such as municipalbonds, corporate bonds, performance bonds, and others), a set of loans(subsidized and unsubsidized, debt transactions and the like,monitoring, classifying, predicting, or otherwise handling thereliability, quality, status, health condition, financial condition,physical condition or other information about a guarantee, a guarantor,a set of collateral supporting a guarantee, a set of assets backing aguarantee, or the like. Bond transaction activities in response to acondition of the bond may include offering a debt transaction,underwriting a debt transaction, setting an interest rate, deferring apayment requirement, modifying an interest rate, validating title,managing inspection, recording a change in title, assessing the value ofan asset, calling a loan, closing a transaction, setting terms andconditions for a transaction, providing notices required to be provided,foreclosing on a set of assets, modifying terms and conditions, settinga rating for an entity, syndicating debt, and/or consolidating debt.

One of skill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system, can readilydetermine which aspects of the present disclosure will benefit aparticular application for a classification model, how to choose orcombine classification models to arrive at a condition, and/or calculatea value of collateral given the required data. Certain considerationsfor the person of skill in the art, or embodiments of the presentdisclosure in choosing an appropriate condition to manage, include,without limitation: the legality of the condition given the jurisdictionof the transaction, the data available for a given collateral, theanticipated transaction type (loan, bond or debt), the specific type ofcollateral, the ratio of the loan to value, the ratio of the collateralto the loan, the gross transaction/loan amount, the credit scores of theborrower and the lender, and other considerations. While specificexamples of conditions, condition classification, classification models,and condition management are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The terms classify, classifying, classification, categorization,categorizing, categorize (and similar terms) as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, classifying a condition or itemmay include actions to sort the condition or item into a group orcategory based on some aspect, attribute, or characteristic of thecondition or item where the condition or item is common or similar forall the items placed in that classification, despite divergentclassifications or categories based on other aspects or conditions atthe time. Classification may include recognition of one or moreparameters, features, characteristics, or phenomena associated with acondition or parameter of an item, entity, person, process, item,financial construct, or the like. Conditions classified by a conditionclassifying system may include a default condition, a foreclosurecondition, a condition indicating violation of a covenant, a financialrisk condition, a behavioral risk condition, a contractual performancecondition, a policy risk condition, a financial health condition, aphysical defect condition, a physical health condition, an entity riskcondition, and/or an entity health condition. A classification model mayautomatically classify or categorize items, entities, process, items,financial constructs or the like based on data received from a varietyof sources. The classification model may classify items based on asingle attribute or a combination of attributes, and/or may utilize dataregarding the items to be classified and a model. The classificationmodel may classify individual items, entities, financial constructs orgroups of the same. A bond may be classified based on the type of bond((e.g. municipal bonds, corporate bonds, performance bonds, and thelike), rate of return, bond rating (3^(rd) party indicator of bondquality with respect to bond issuer's financial strength, and/or abilityto bap bond's principal and interest, and the like. Lenders or bondissuers may be classified based on the type of lender or issuer,permitted attributes (e.g. based on income, wealth, location (domesticor foreign), various risk factors, status of issuers, and the like.Borrowers may be classified based on permitted attributes (e.g. income,wealth, total assets, location, credit history), risk factors, currentstatus (e.g. employed, a student), behaviors of parties (such asbehaviors indicating preferences, reliability, and the like), and thelike. A condition classifying system may classify a student recipient ofa loan based on progress of the student toward a degree, the student'sgrades or standing in their classes, student's status at the school(matriculated, on probation and the like), the participation of astudent in a non-profit activity, a deferment status of the student, andthe participation of the student in a public interest activity.Conditions classified by a condition classifying system may include astate of a set of collateral for a loan or a state of an entity relevantto a guarantee for a loan. Conditions classified by a conditionclassifying system may include a medical condition of a borrower,guarantor, subsidizer or the like. Conditions classified by a conditionclassifying system may include compliance with at least one of a law, aregulation, or a policy related to a lending transaction or lendinginstitute. Conditions classified by a condition classifying system mayinclude a condition of an issuer for a bond, a condition of a bond, arating of a loan-related entity, and the like. Conditions classified bya condition classifying system may include an identify of a machine, acomponent, or an operational mode. Conditions classified by a conditionclassifying system may include a state or context (such as a state of amachine, a process, a workflow, a marketplace, a storage system, anetwork, a data collector, or the like). A condition classifying systemmay classify a process involving a state or context (e.g., a datastorage process, a network coding process, a network selection process,a data marketplace process, a power generation process, a manufacturingprocess, a refining process, a digging process, a boring process, and/orother process described herein. A condition classifying system mayclassify a set of loan refinancing actions based on a predicted outcomeof the set of loan refinancing actions. A condition classifying systemmay classify a set of loans as candidates for consolidation based onattributes such as identity of a party, an interest rate, a paymentbalance, payment terms, payment schedule, a type of loan, a type ofcollateral, a financial condition of party, a payment status, acondition of collateral, a value of collateral, and the like. Acondition classifying system may classify the entities involved in a setof factoring loans, bond issuance activities, mortgage loans, and thelike. A condition classifying system may classify a set of entitiesbased on projected outcomes from various loan management activities. Acondition classifying system may classify a condition of a set ofissuers based on information from Internet of Things data collection andmonitoring services, a set of parameters associated with an issuer, aset of social network monitoring and analytic services, and the like. Acondition classifying system may classify a set of loan collectionactions, loan consolidation actions, loan negotiation actions, loanrefinancing actions and the like based on a set of projected outcomesfor those activities and entities.

The term subsidized loan, subsidizing a loan, (and similar terms) asutilized herein should be understood broadly. Without limitation to anyother aspect or description of the present disclosure, a subsidized loanis the loan of money or an item of value wherein payment of interest onthe value of the loan may be deferred, postponed or delayed, with orwithout accrual, such as while the borrower is in school, is unemployed,is ill, and the like. In embodiments, a loan may be subsidized when thepayment of interest on a portion or subset of the loan is borne orguaranteed by someone other than the borrower. Examples of subsidizedloans may include a municipal subsidized loan, a government subsidizedloan, a student loan, an asset-backed subsidized loan, and a corporatesubsidized loan. An example of a subsidized student loan may includestudent loans which may be subsidized by the government and on whichinterest may be deferred or not accrue based on progress of the studenttoward a degree, the participation of a student in a non-profitactivity, a deferment status of the student, and the participation ofthe student in a public interest activity. An example of a governmentsubsidized housing loan may include governmental subsidies which mayexempt the borrower from paying closing costs, first mortgage paymentand the like. Conditions for such subsidized loans may include locationof the property (rural or urban), income of the borrower, militarystatus of the borrower, ability of the purchased home to meet health andsafety standards, a limit on the profits you can earn on the sale ofyour home, and the like. Certain usages of the word loan may not applyto a subsidized loan but rather to a regular loan. One of skill in theart, having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit fromconsideration of a subsidized loan (e.g., in determining the value ofthe loan, negotiations related to the loan, terms and conditions relatedto the loan, etc.) wherein the borrower may be relieved of some of theloan obligations common for non-subsidized loans, where the subsidy mayinclude forgiveness, delay or deferment of interest on a loan, or thepayment of the interest by a third party. The subsidy may include thepayment of closing costs including points, first payment and the like bya person or entity other than the borrower, and/or how to combineprocesses and systems from the present disclosure to enhance or benefitfrom title validation.

The term subsidized loan management (and similar terms) as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, subsidized loanmanagement may include a plurality of activities and solutions formanaging or responding to one or more events related to a subsidizedloan wherein such events may include requests for a subsidized loan,offering a subsidized loan, accepting a subsidized loan, providingunderwriting information for a subsidized loan, providing a creditreport on a borrower seeking a subsidized loan, deferring a requiredpayment as part of the loan subsidy, setting an interest rate for asubsidized loan where a lower interest rate may be part of the subsidy,deferring a payment requirement as part of the loan subsidy, identifyingcollateral for a loan, validating title for collateral or security for aloan, recording a change in title of property, assessing the value ofcollateral or security for a loan, inspecting property that is involvedin a loan, identifying a change in condition of an entity relevant to aloan, a change in value of an entity that is relevant to a loan, achange in job status of a borrower, a change in financial rating of alender, a change in financial value of an item offered as a security,providing insurance for a loan, providing evidence of insurance forproperty related to a loan, providing evidence of eligibility for aloan, identifying security for a loan, underwriting a loan, making apayment on a loan, defaulting on a loan, calling a loan, closing a loan,setting terms and conditions for a loan, foreclosing on property subjectto a loan, modifying terms and conditions for a loan, for setting termsand conditions for a loan (such as a principal amount of debt, a balanceof debt, a fixed interest rate, a variable interest rate, a paymentamount, a payment schedule, a balloon payment schedule, a specificationof collateral, a specification of substitutability of collateral, aparty, a guarantee, a guarantor, a security, a personal guarantee, alien, a duration, a covenant, a foreclose condition, a defaultcondition, and a consequence of default), or managing loan-relatedactivities (such as, without limitation, finding parties interested inparticipating in a loan transaction, handling an application for a loan,underwriting a loan, forming a legal contract for a loan, monitoringperformance of a loan, making payments on a loan, restructuring oramending a loan, settling a loan, monitoring collateral for a loan,forming a syndicate for a loan, foreclosing on a loan, collecting on aloan, consolidating a set of loans, analyzing performance of a loan,handling a default of a loan, transferring title of assets orcollateral, and closing a loan transaction), and the like. Inembodiments, a system for handling a subsidized loan may includeclassifying a set of parameters of a set of subsidized loans on thebasis of data relating to those parameters obtained from an Internet ofThings data collection and monitoring service. Classifying the set ofparameters of the set of subsidized loans may also be on the bases ofdata obtained from one or more configurable data collection andmonitoring services that leverage social network analytic services,crowd sourcing services, and the like for obtaining parameter data(e.g., determination that a person or entity is qualified for thesubsidized loan, determining a social value of providing the subsidizedloan or removing a subsidization from a loan, determining that asubsidizing entity is legitimate, determining appropriate subsidizationterms based on characteristics of the buyer and/or subsidizer, etc.).

The term foreclose, foreclosure, foreclose or foreclosure condition,default foreclosure collateral, default collateral, (and similar terms)as utilized herein should be understood broadly. Without limitation toany other aspect or description of the present disclosure, foreclosecondition, default and the like describe the failure of a borrower tomeet the terms of a loan. Without limitation to any other aspect ordescription of the present disclosure foreclose and foreclosure includeprocesses by which a lender attempts to recover, from a borrower in aforeclose or default condition, the balance of a loan or take away inlieu, the right of a borrower to redeem a mortgage held in security forthe loan. Failure to meet the terms of the loan may include failure tomake specified payments, failure to adhere to a payment schedule,failure to make a balloon payment, failure to appropriately secure thecollateral, failure to sustain collateral in a specified condition (e.g.in good repair), acquisition of a second loan, and the like. Foreclosuremay include a notification to the borrower, the public, jurisdictionalauthorities of the forced sale of an item collateral such as through aforeclosure auction. Upon foreclosure, an item of collateral may beplaced on a public auction site (such as eBay™ or an auction siteappropriate for a particular type of property. The minimum opening bidfor the item of collateral may be set by the lender and may cover thebalance of the loan, interest on the loan, fees associated with theforeclosure and the like. Attempts to recover the balance of the loanmay include the transfer of the deed for an item of collateral in lieuof foreclosure (e.g. a real-estate mortgage where the borrower holds thedeed for a property which acts as collateral for the mortgage loan).Foreclosure may include taking possession of or repossessing thecollateral (e.g. a car, a sports vehicle such as a boat, ATV,ski-mobile, jewelry). Foreclosure may include securing an item ofcollateral associated with the loan (such as by locking a connecteddevice, such as a smart lock, smart container, or the like that containsor secures collateral). Foreclosure may include arranging for theshipping of an item of collateral by a carrier, freight forwarder of thelike. Foreclosure may include arranging for the transport of an item ofcollateral by a drone, a robot, or the like for transporting collateral.In embodiments, a loan may allow for the substitution of collateral orthe shifting of the lien from an item of collateral initially used tosecure the loan to a substitute collateral where the substitutecollateral is of higher value (to the lender) than the initialcollateral or is an item in which the borrower has a greater equity. Theresult of the substitution of collateral is that when the loan goes intoforeclosure, it is the substitute collateral that may be the subject ofa forced sale or seizure. Certain usages of the word default may notapply to such as to foreclose but rather to a regular or defaultcondition of an item. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit from foreclosure, and/or how to combineprocesses and systems from the present disclosure to enhance or benefitfrom foreclosure. Certain considerations for the person of skill in theart, in determining whether the term foreclosure, foreclose condition,default and the like is referring to failure of a borrower to meet theterms of a loan and the related attempts by the lender to recover thebalance of the loan or obtain ownership of the collateral.

The terms validation of title, title validation, validating title, andsimilar terms, as utilized herein should be understood broadly. Withoutlimitation to any other aspect or description of the present disclosurevalidation of title and title validation include any efforts to verifyor confirm the ownership or interest by an individual or entity in anitem of property such as a vehicle, a ship, a plane, a building, a home,real estate property, undeveloped land, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property. Efforts to verify ownership may include reference tobills of sale, government documentation of transfer of ownership, alegal will transferring ownership, documentation of retirement of lienson the item of property, verification of assignment of IntellectualProperty to the proposed borrower in the appropriate jurisdiction, andthe like. For real-estate property validation may include a review ofdeeds and records at a courthouse of a country, a state, a county or adistrict in which a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a vehicle, a ship, a plane,or a warehouse is located or registered. Certain usages of the wordvalidation may not apply to validation of a title or title validationbut rather to confirmation that a process is operating correctly, thatan individual has been correctly identified using biometric data, thatintellectual property rights are in effect, that data is correct andmeaningful, and the like. One of skill in the art, having the benefit ofthe disclosure herein and knowledge about a contemplated systemordinarily available to that person, can readily determine which aspectsof the present disclosure will benefit from title validation, and/or howto combine processes and systems from the present disclosure to enhanceor benefit from title validation. Certain considerations for the personof skill in the art, in determining whether the term validation isreferring to title validation, are specifically contemplated within thescope of the present disclosure.

Without limitation to any other aspect or description of the presentdisclosure, validation includes any validating system including, withoutlimitation, validating title for collateral or security for a loan,validating conditions of collateral for security or a loan, validatingconditions of a guarantee for a loan, and the like. For instance, avalidation service may provide lenders a mechanism to deliver loans withmore certainty, such as through validating loan or security informationcomponents (e.g., income, employment, title, conditions for a loan,conditions of collateral, and conditions of an asset). In a non-limitingexample, a validation service circuit may be structured to validate aplurality of loan information components with respect to a financialentity configured to determine a loan condition for an asset. Certaincomponents may not be considered a validating system individually, butmay be considered validating in an aggregated system—for example, anInternet of Things component may not be considered a validatingcomponent on its own, however an Internet of Things component utilizedfor asset data collection and monitoring may be considered a validatingcomponent when applied to validating a reliability parameter of apersonal guarantee for a load when the Internet of Things component isassociated with a collateralized asset. In certain embodiments,otherwise similar looking systems may be differentiated in determiningwhether such systems are for validation. For example, a blockchain-basedledger may be used to validate identities in one instance and tomaintain confidential information in another instance. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofsystems, and any such systems may be considered a system for validationherein, while in certain embodiments a given system may not beconsidered a validating system herein. One of skill in the art, havingthe benefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit a particular system,and/or how to combine processes and systems from the present disclosureto enhance operations of the contemplated system. Certain considerationsfor the person of skill in the art, in determining whether acontemplated system is a validating system and/or whether aspects of thepresent disclosure can benefit or enhance the contemplated systeminclude, without limitation: a lending platform having a social networkmonitoring system for validating the reliability of a guarantee for aloan; a lending platform having an Internet of Things data collectionand monitoring system for validating reliability of a guarantee for aloan; a lending platform having a crowdsourcing and automatedclassification system for validating conditions of an issuer for a bond;a crowdsourcing system for validating quality, title, or otherconditions of collateral for a loan; a biometric identify validationapplication such as utilizing DNA or fingerprints; IoT devices utilizedto collectively validate location and identity of a fixed asset that istagged by a virtual asset tag; validation systems utilizing voting orconsensus protocols; artificial intelligence systems trained torecognize and validate events; validating information such as titlerecords, video footage, photographs, or witnessed statements; validationrepresentations related to behavior, such as to validate occurrence ofconditions of compliance, to validate occurrence of conditions ofdefault, to deter improper behavior or misrepresentations, to reduceuncertainty, or to reduce asymmetries of information; and the like.

The term underwriting (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, underwriting includes anyunderwriting, including, without limitation, relating to underwriters,providing underwriting information for a loan, underwriting a debttransaction, underwriting a bond transaction, underwriting a subsidizedloan transaction, underwriting a securities transaction, and the like.Underwriting services may be provided by financial entities, such asbanks, insurance or investment houses, and the like, whereby thefinancial entity guarantees payment in case of a determination of a losscondition (e.g., damage or financial loss) and accept the financial riskfor liability arising from the guarantee. For instance, a bank mayunderwrite a loan through a mechanism to perform a credit analysis thatmay lead to a determination of a loan to be granted, such as throughanalysis of personal information components related to an individualborrower requesting a consumer loan (e.g., employment history, salaryand financial statements publicly available information such as theborrower's credit history), analysis of business financial informationcomponents from a company requesting a commercial load (e.g., tangiblenet worth, ratio of debt to worth (leverage), and available liquidity(current ratio)), and the like. In a non-limiting example, anunderwriting services circuit may be structured to underwrite afinancial transaction including a plurality of financial informationcomponents with respect to a financial entity configured to determine afinancial condition for an asset. In certain embodiments, underwritingcomponents may be considered underwriting for some purposes but not forother purposes—for example, an artificial intelligence system to collectand analyze transaction data may be utilized in conjunction with a smartcontract platform to monitor loan transactions, but alternately used tocollect and analyze underwriting data, such as utilizing a model trainedby human expert underwriters. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of systems, and any suchsystems may be considered underwriting herein, while in certainembodiments a given system may not be considered underwriting herein.One of skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, and/or how to combine processes andsystems from the present disclosure to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is underwritingand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation: a lending platformhaving an underwriting system for a loan with a set of data-integratedmicroservices such as including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions;underwriting processes, operations, and services; underwriting data,such as data relating to identities of prospective and actual partiesinvolved insurance and other transactions, actuarial data, data relatingto probability of occurrence and/or extent of risk associated withactivities, data relating to observed activities and other data used tounderwrite or estimate risk; an underwriting application, such as,without limitation, for underwriting any insurance offering, any loan,or any other transaction, including any application for detecting,characterizing or predicting the likelihood and/or scope of a risk, anunderwriting or inspection flow about an entity serving a lendingsolution, an analytics solution, or an asset management solution;underwriting of insurance policies, loans, warranties, or guarantees; ablockchain and smart contract platform for aggregating identity andbehavior information for insurance underwriting, such as with anoptional distributed ledger to record a set of events, transactions,activities, identities, facts, and other information associated with anunderwriting process; a crowdsourcing platform such as for underwritingof various types of loans, and guarantees; an underwriting system for aloan with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions; an underwriting solution to create,configure, modify, set or otherwise handle various rules, thresholds,conditional procedures, workflows, or model parameters; an underwritingaction or plan for management a set of loans of a given type or typesbased on one or more events, conditions, states, actions, secondaryloans or transactions to back loans, for collection, consolidation,foreclosure, situations of bankruptcy of insolvency, modifications ofexisting loans, situations involving market changes, foreclosureactivities; adaptive intelligent systems including artificialintelligent models trained on a training set of underwriting activitiesby experts and/or on outcomes of underwriting actions to generate a setof predictions, classifications, control instructions, plans, models;underwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions;and the like.

The term insuring (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, insuring includes any insuring,including, without limitation, providing insurance for a loan, providingevidence of insurance for an asset related to a loan, a first entityaccepting a risk or liability for another entity, and the like.Insuring, or insurance, may be a mechanism through which a holder of theinsurance is provided protection from a financial loss, such as in aform of risk management against the risk of a contingent or uncertainloss. The insuring mechanism may provide for an insurance, determine theneed for an insurance, determine evidence of insurance, and the like,such as related to an asset, transaction for an asset, loan for anasset, security, and the like. An entity which provides insurance may beknown as an insurer, insurance company, insurance carrier, underwriter,and the like. For instance, a mechanism for insuring may provide afinancial entity with a mechanism to determine evidence of insurance foran asset related to a loan. In a non-limiting example, an insuranceservice circuit may be structured to determine an evidence condition ofinsurance for an asset based on a plurality of insurance informationcomponents with respect to a financial entity configured to determine aloan condition for an asset. In certain embodiments, components may beconsidered insuring for some purposes but not for other purposes—forexample, a blockchain and smart contract platform may be utilized tomanage aspects of a loan transaction such as for identity andconfidentiality, but may alternately be utilized to aggregate identityand behavior information for insurance underwriting. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofsystems, and any such systems may be considered insuring herein, whilein certain embodiments a given system may not be considered insuringherein. One of skill in the art, having the benefit of the disclosureherein and knowledge about a contemplated system ordinarily available tothat person, can readily determine which aspects of the presentdisclosure will benefit a particular system, and/or how to combineprocesses and systems from the present disclosure to enhance operationsof the contemplated system. Certain considerations for the person ofskill in the art, in determining whether a contemplated system isinsuring and/or whether aspects of the present disclosure can benefit orenhance the contemplated system include, without limitation: insurancefacilities such as branches, offices, storage facilities, data centers,underwriting operations and others; insurance claims, such as forbusiness interruption insurance, product liability insurance, insuranceon goods, facilities, or equipment, flood insurance, insurance forcontract-related risks, and many others, as well as claims data relatingto product liability, general liability, workers compensation, injuryand other liability claims and claims data relating to contracts, suchas supply contract performance claims, product delivery requirements,contract claims, claims for damages, claims to redeem points or rewards,claims of access rights, warranty claims, indemnification claims, energyproduction requirements, delivery requirements, timing requirements,milestones, key performance indicators and others; insurance-relatedlending; an insurance service, an insurance brokerage service, a lifeinsurance service, a health insurance service, a retirement insuranceservice, a property insurance service, a casualty insurance service, afinance and insurance service, a reinsurance service; a blockchain andsmart contract platform for aggregating identity and behaviorinformation for insurance underwriting; identities of applicants forinsurance, identities of parties that may be willing to offer insurance,information regarding risks that may be insured (of any type, withoutlimitation, such as property, life, travel, infringement, health, home,commercial liability, product liability, auto, fire, flood, casualty,retirement, unemployment; distributed ledger may be utilized tofacilitate offering and underwriting of microinsurance, such as fordefined risks related to defined activities for defined time periodsthat are narrower than for typical insurance policies; providinginsurance for a loan, providing evidence of insurance for propertyrelated to a loan; and the like.

The term aggregation (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, an aggregation or to aggregateincludes any aggregation including, without limitation, aggregatingitems together, such as aggregating or linking similar items together(e.g., collateral to provide collateral for a set of loans, collateralitems for a set of loans is aggregated in real time based on asimilarity in status of the set of items, and the like), collecting datatogether (e.g., for storage, for communication, for analysis, astraining data for a model, and the like), summarizing aggregated itemsor data into a simpler description, or any other method for creating awhole formed by combining several (e.g., disparate) elements. Further,an aggregator may be any system or platform for aggregating, such asdescribed. Certain components may not be considered aggregationindividually but may be considered aggregation in an aggregatedsystem—for example, a collection of loans may not be considered anaggregation of loans of itself but may be an aggregation if collected assuch. In a non-limiting example, an aggregation circuit may bestructured to provide lenders a mechanism to aggregate loans togetherfrom a plurality of loans, such as based on a loan attribute, parameter,term or condition, financial entity, and the like, to become anaggregation of loans. In certain embodiments, an aggregation may beconsidered an aggregation for some purposes but not for otherpurposes—for example for example, an aggregation of asset collateralconditions may be collected for the purpose of aggregating loanstogether in one instance and for the purpose of determining a defaultaction in another instance. Additionally, in certain embodiments,otherwise similar looking systems may be differentiated in determiningwhether such systems are aggregators, and/or which type of aggregatingsystems. For example, a first and second aggregator may both aggregatefinancial entity data, where the first aggregator aggregates for thesake of building a training set for an analysis model circuit and wherethe second aggregator aggregates financial entity data for storage in ablockchain-based distributed ledger. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered as aggregation herein, while in certainembodiments a given system may not be considered aggregation herein. Oneof skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, and/or how to combine processes andsystems from the present disclosure to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is aggregationand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation forward marketdemand aggregation (e.g., blockchain and smart contract platform forforward market demand aggregation, interest expressed or committed in ademand aggregation interface, blockchain used to aggregate future demandin a forward market with respect to a variety of products and services,process a set of potential configurations having different parametersfor a subset of configurations that are consistent with each other andthe subset of configurations used to aggregate committed future demandfor the offering that satisfies a sufficiently large subset at aprofitable price, and the like); correlated aggregated data (includingtrend information) on worker ages, credentials, experience (including byprocess type) with data on the processes in which those workers areinvolved; demand for accommodations aggregated in advance andconveniently fulfilled by automatic recognition of conditions thatsatisfy pre-configured commitments represented on a blockchain (e.g.,distributed ledger); transportation offerings aggregated and fulfilled(e.g., with a wide range of pre-defined contingencies); aggregation ofgoods and services on the blockchain (e.g., a distributed ledger usedfor demand planning); with respect to a demand aggregation interface(e.g., presented to one or more consumers); aggregation of multiplesubmissions; aggregating identity and behavior information (e.g.,insurance underwriting); accumulation and aggregation of multipleparties; aggregation of data for a set of collateral; aggregated valueof collateral or assets (e.g., based on real time condition monitoring,real-time market data collection and integration, and the like);aggregated tranches of loans; collateral for smart contract aggregatedwith other similar collateral; and the like.

The term linking (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, linking includes any linking,including, without limitation, linking as a relationship between twothings or situations (e.g., where one thing affects the other). Forinstance, linking a subset of similar items such as collateral toprovide collateral for a set of loans. Certain components may not beconsidered linked individually, but may be considered in a process oflinking in an aggregated system—for example, a smart contracts circuitmay be structured to operate in conjunction with a blockchain circuit aspart of a loan processing platform but where the smart contracts circuitprocesses contracts without storing information through the blockchaincircuit, however the two circuits could be linked through the smartcontracts circuit linking financial entity information through adistributed ledger on the blockchain circuit. In certain embodiments,linking may be considered linking for some purposes but not for otherpurposes—for example, linking goods and services for users and radiofrequency linking between access points are different forms of linking,where the linking of goods and services for users links things togetherwhile an RF link is a communications link between transceivers.Additionally, in certain embodiments, otherwise similar looking systemsmay be differentiated in determining whether such system are linking,and/or which type of linking. For example, linking similar data togetherfor analysis is different from linking similar data together forgraphing. Accordingly, the benefits of the present disclosure may beapplied in a wide variety of systems, and any such systems may beconsidered linking herein, while in certain embodiments a given systemmay not be considered a linking herein. One of skill in the art, havingthe benefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit a particular system,and/or how to combine processes and systems from the present disclosureto enhance operations of the contemplated system. Certain considerationsfor the person of skill in the art, in determining whether acontemplated system is linking and/or whether aspects of the presentdisclosure can benefit or enhance the contemplated system include,without limitation linking marketplaces or external marketplaces with asystem or platform; linking data (e.g., data cluster including links andnodes); storage and retrieval of data linked to local processes; links(e.g. with respect to nodes) in a common knowledge graph; data linked toproximity or location (e.g., of the asset); linking to an environment(e.g., goods, services, assets, and the like); linking events (e.g., forstorage such as in a blockchain, for communication or analysis); linkingownership or access rights; linking to access tokens (e.g., travelofferings linked to access tokens); links to one or more resources(e.g., secured by cryptographic or other techniques); linking a messageto a smart contract; and the like.

The term indicator of interest (and similar terms) as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, an indicator of interest includesany indicator of interest including, without limitation, an indicator ofinterest from a user or plurality of users or parties related to atransaction and the like (e.g., parties interested in participating in aloan transaction), the recording or storing of such an interest (e.g., acircuit for recording an interest input from a user, entity, circuit,system, and the like), a circuit analyzing interest related data andsetting an indicator of interest (e.g., a circuit setting orcommunicating an indicator based on inputs to the circuit, such as fromusers, parties, entities, systems, circuits, and the like), a modeltrained to determine an indicator of interest from input data related toan interest by one of a plurality of inputs from users, parties, orfinancial entities, and the like. Certain components may not beconsidered indicators of interest individually, but may be considered anindicator of interest in an aggregated system—for example, a party mayseek information relating to a transaction such as though a translationmarketplace where the party is interested in seeking information, butthat may not be considered an indicator of interest in a transaction.However, when the party asserts a specific interest (e.g., through auser interface with control inputs for indicating interest) the party'sinterest may be recorded (e.g., in a storage circuit, in a blockchaincircuit), analyzed (e.g., through an analysis circuit, a data collectioncircuit), monitored (e.g., through a monitoring circuit), and the like.In a non-limiting example, indicators of interest may be recorded (e.g.,in a blockchain through a distributed ledger) from a set of parties withrespect to the product, service, or the like, such as ones that defineparameters under which a party is willing to commit to purchase aproduct or service. In certain embodiments, an indicator of interest maybe considered an indicator of interest for some purposes but not forother purposes—for example, a user may indicate an interest for a loantransaction but that does not necessarily mean the user is indicating aninterest in providing a type of collateral related to the loantransaction. For instance, a data collection circuit may record anindicator of interest for the transaction but may have a separatecircuit structure for determining an indication of interest forcollateral. Additionally, in certain embodiments, otherwise similarlooking systems may be differentiated in determining whether such systemare determining an indication of interest, and/or which type ofindicator of interest exists. For example, one circuit or system maycollect data from a plurality of parties to determine an indicator ofinterest in securing a loan and a second circuit or system may collectdata from a plurality of parties to determine an indicator of interestin a determining ownership rights related to a loan. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofsystems, and any such systems may be considered an indicator of interestherein, while in certain embodiments a given system may not beconsidered an indicator of interest herein. One of skill in the art,having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.Certain considerations for the person of skill in the art, indetermining whether a contemplated system is an indicator of interestand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation parties indicatingan interest in participating in a transaction (e.g., a loantransaction), parties indicating an interest in securing in a product orservice, recording or storing an indication of interest (e.g., through astorage circuit or blockchain circuit), analyzing an indication ofinterest (e.g., through a data collection and/or monitoring circuit),and the like.

The term accommodations (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, an accommodation includes anyservice, activity, event, and the like such as including, withoutlimitation, a room, group of rooms, table, seating, building, event,shared spaces offered by individuals (e.g., Airbnb™ spaces),bed-and-breakfasts, workspaces, conference rooms, convention spaces,fitness accommodations, health and wellness accommodations, diningaccommodations, and the like, in which someone may live, stay, sit,reside, participate, and the like. As such, an accommodation may bepurchased (e.g., a ticket through a sports ticketing application),reserved or booked (e.g., a reservation through a hotel reservationapplication), provided as a reward or gift, traded or exchanged (e.g.,through a marketplace), provided as an access right (e.g., offering byway of an aggregation demand), provided based on a contingency (e.g., areservation for a room being contingent on the availability of a nearbyevent), and the like. Certain components may not be considered anaccommodation individually but may be considered an accommodation in anaggregated system—for example, a resource such as a room in a hotel maynot in itself be considered an accommodation but a reservation for theroom may be. For instance, a blockchain and smart contract platform forforward market rights for accommodations may provide a mechanism toprovide access rights with respect to accommodations. In a non-limitingexample, a blockchain circuit may be structured to store access rightsin a forward demand market, where the access rights may be stored in adistributed ledger with related shared access to a plurality ofactionable entities. In certain embodiments, an accommodation may beconsidered an accommodation for some purposes but not for otherpurposes—for example, a reservation for a room may be an accommodationon its own, but may not be accommodation that is satisfied if a relatedcontingency is not met as agreed upon at the time of the e.g.reservation. Additionally, in certain embodiments, otherwise similarlooking systems may be differentiated in determining whether suchsystems are related to an accommodation, and/or which type ofaccommodation. For example, an accommodation offering may be made basedon different systems, such as one where the accommodation offering isdetermined by a system collecting data related to forward demand and asecond one where the accommodation offering is provided as a rewardbased on a system processing a performance parameter. Accordingly, thebenefits of the present disclosure may be applied in a wide variety ofsystems, and any such systems may be considered as related to anaccommodation herein, while in certain embodiments a given system maynot be considered related to an accommodation herein. One of skill inthe art, having the benefit of the disclosure herein and knowledge abouta contemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.Certain considerations for the person of skill in the art, indetermining whether a contemplated system is related to accommodationand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation an accommodationsprovided as determined through a service circuit, trading or exchangingservices (e.g., through an application and/or user interface), as anaccommodation offering such as with respect to a combination ofproducts, services, and access rights, processed (e.g., aggregationdemand for the offering in a forward market), accommodation throughbooking in advance, accommodation through booking in advance uponmeeting a certain condition (e.g., relating to a price within a giventime window), and the like.

The term contingencies (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a contingency includes anycontingency including, without limitation, any action that is dependentupon a second action. For instance, a service may be provided ascontingent on a certain parameter value, such as collecting data ascondition upon an asset tag indication from an Internet of Thingscircuit. In another instance, an accommodation such as a hotelreservation may be contingent upon a concert (local to the hotel and atthe same time as the reservation) proceeding as scheduled. Certaincomponents may not be considered as relating to a contingencyindividually, but may be considered related to a contingency in anaggregated system—for example, a data input collected from a datacollection service circuit may be stored, analyzed, processed, and thelike, and not be considered with respect to a contingency, however asmart contracts service circuit may apply a contract term as beingcontingent upon the collected data. For instance, the data may indicatea collateral status with respect to a loan transaction, and the smartcontracts service circuit may apply that data to a term of contract thatdepends upon the collateral. In certain embodiments, a contingency maybe considered contingency for some purposes but not for otherpurposes—for example, a delivery of contingent access rights for afuture event may be contingent upon a loan condition being satisfied,but the loan condition on its own may not be considered a contingency inthe absence of the contingency linkage between the condition and theaccess rights. Additionally, in certain embodiments, otherwise similarlooking systems may be differentiated in determining whether suchsystems are related to a contingency, and/or which type of contingency.For example, two algorithms may both create a forward market eventaccess right token, but where the first algorithm creates the token freeof contingencies and the second algorithm creates a token with acontingency for delivery of the token. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered a contingency herein, while in certainembodiments a given system may not be considered a contingency herein.One of skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, and/or how to combine processes andsystems from the present disclosure to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is a contingencyand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation a forward marketoperated within or by the platform may be a contingent forward market,such as one where a future right is vested, is triggered, or emergesbased on the occurrence of an event, satisfaction of a condition, or thelike; a blockchain used to make a contingent market in any form of eventor access token by securely storing access rights on a distributedledger; setting and monitoring pricing for contingent access rights,underlying access rights, tokens, fees and the like; optimizingofferings, timing, pricing, or the like, to recognize and predictpatterns, to establish rules and contingencies; exchanging contingentaccess rights or underlying access rights or tokens access tokens and/orcontingent access tokens; creating a contingent forward market eventaccess right token where a token may be created and stored on ablockchain for contingent access right that could result in theownership of a ticket; discovery and delivery of contingent accessrights to future events; contingencies that influence or representfuture demand for an offering, such as including a set of products,services, or the like; pre-defined contingencies; optimized offerings,timing, pricing, or the like, to recognize and predict patterns, toestablish rules and contingencies; creation of a contingent futureoffering within the dashboard; contingent access rights that may resultin the ownership of the virtual good or each smart contract to purchasethe virtual good if and when it becomes available under definedconditions; and the like.

The term level of service (and similar terms) as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a level of service includes anylevel of service including, without limitation, any qualitative orquantitative measure of the extent to which a service is provided, suchas, and without limitation, a first class vs. business class service(e.g., travel reservation or postal delivery), the degree to which aresource is available (e.g., service level A indicating that theresource is highly available vs. service level C indicating that theresource is constrained, such as in terms of traffic flow restrictionson a roadway), the degree to which an operational parameter isperforming (e.g., a system is operating at a high state of service vs alow state of service, and the like. In embodiments, level of service maybe multi-modal such that the level of service is variable where a systemor circuit provides a service rating (e.g., where the service rating isused as an input to an analytical circuit for determining an outcomebased on the service rating). Certain components may not be consideredrelative to a level of service individually, but may be consideredrelative to a level of service in an aggregated system—for example asystem for monitoring a traffic flow rate may provide data on a currentrate but not indicate a level of service, but when the determinedtraffic flow rate is provided to a monitoring circuit the monitoringcircuit may compare the determined traffic flow rate to past trafficflow rates and determine a level of service based on the comparison. Incertain embodiments, a level of service may be considered a level ofservice for some purposes but not for other purposes—for example, theavailability of first class travel accommodation may be considered alevel of service for determining whether a ticket will be purchased butnot to project a future demand for the flight. Additionally, in certainembodiments, otherwise similar looking systems may be differentiated indetermining whether such system utilizes a level of service, and/orwhich type of level of service. For example, an artificial intelligencecircuit may be trained on past level of service with respect to trafficflow patterns on a certain freeway and used to predict future trafficflow patterns based on current flow rates, but a similar artificialintelligence circuit may predict future traffic flow patterns based onthe time of day. Accordingly, the benefits of the present disclosure maybe applied in a wide variety of systems, and any such systems may beconsidered with respect to levels of service herein, while in certainembodiments a given system may not be considered with respect to levelsof service herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, and/or how tocombine processes and systems from the present disclosure to enhanceoperations of the contemplated system. Certain considerations for theperson of skill in the art, in determining whether a contemplated systemis a level of service and/or whether aspects of the present disclosurecan benefit or enhance the contemplated system include, withoutlimitation transportation or accommodation offerings with predefinedcontingencies and parameters such as with respect to price, mode ofservice, and level of service; warranty or guarantee application,transportation marketplace, and the like.

The term payment (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a payment includes any paymentincluding, without limitation, an action or process of paying (e.g., apayment to a loan) or of being paid (e.g., a payment from insurance), anamount paid or payable (e.g., a payment of $1000 being made), arepayment (e.g., to pay back a loan), a mode of payment (e.g., use ofloyalty programs, rewards points, or particular currencies, includingcryptocurrencies) and the like. Certain components may not be consideredpayments individually, but may be considered payments in an aggregatedsystem—for example, submitting an amount of money may not be considereda payment as such, but when applied to a payment to satisfy therequirement of a loan may be considered a payment (or repayment). Forinstance, a data collection circuit may provide lenders a mechanism tomonitor repayments of a loan. In a non-limiting example, the datacollection circuit may be structured to monitor the payments of aplurality of loan components with respect to a financial loan contractconfigured to determine a loan condition for an asset. In certainembodiments, a payment may be considered a payment for some purposes butnot for other purposes—for example, a payment to a financial entity maybe for a repayment amount to pay back the loan, or it may be to satisfya collateral obligation in a loan default condition. Additionally, incertain embodiments, otherwise similar looking systems may bedifferentiated in determining whether such system are related to apayment, and/or which type of payment. For example, funds may be appliedto reserve an accommodation or to satisfy the delivery of services afterthe accommodation has been satisfied. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered a payment herein, while in certainembodiments a given system may not be considered a payment herein. Oneof skill in the art, having the benefit of the disclosure herein andknowledge about a contemplated system ordinarily available to thatperson, can readily determine which aspects of the present disclosurewill benefit a particular system, and/or how to combine processes andsystems from the present disclosure to enhance operations of thecontemplated system. Certain considerations for the person of skill inthe art, in determining whether a contemplated system is a paymentand/or whether aspects of the present disclosure can benefit or enhancethe contemplated system include, without limitation, deferring arequired payment; deferring a payment requirement; payment of a loan; apayment amount; a payment schedule; a balloon payment schedule; paymentperformance and satisfaction; modes of payment; and the like.

The term location (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a location includes any locationincluding, without limitation, a particular place or position of aperson, place, or item, or location information regarding the positionof a person, place, or item, such as a geolocation (e.g., geolocation ofa collateral), a storage location (e.g., the storage location of anasset), a location of a person (e.g., lender, borrower, worker),location information with respect to the same, and the like. Certaincomponents may not be considered with respect to location individually,but may be considered with respect to location in an aggregatedsystem—for example, a smart contract circuit may be structured tospecify a requirement for a collateral to be stored at a fixed locationbut not specify the specific location for a specific collateral. Incertain embodiments, a location may be considered a location for somepurposes but not for other purposes—for example, the address location ofa borrower may be required for processing a loan in one instance, and aspecific location for processing a default condition in anotherinstance. Additionally, in certain embodiments, otherwise similarlooking systems may be differentiated in determining whether such systemare a location, and/or which type of location. For example, the locationof a music concert may be required to be in a concert hall seating10,000 people in one instance but specify the location of an actualconcert hall in another. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of systems, and any suchsystems may be considered with respect to a location herein, while incertain embodiments a given system may not be considered with respect toa location herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, and/or how tocombine processes and systems from the present disclosure to enhanceoperations of the contemplated system. Certain considerations for theperson of skill in the art, in determining whether a contemplated systemis considered with respect to a location and/or whether aspects of thepresent disclosure can benefit or enhance the contemplated systeminclude, without limitation a geolocation of an item or collateral; astorage location of item or asset; location information; location of alender or a borrower; location-based product or service targetingapplication; a location-based fraud detection application; indoorlocation monitoring systems (e.g., cameras, IR systems, motion-detectionsystems); locations of workers (including routes taken through alocation); location parameters; event location; specific location of anevent; and the like.

The term route (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, a route includes any routeincluding, without limitation, a way or course taken in getting from astarting point to a destination, to send or direct along a specifiedcourse, and the like. Certain components may not be considered withrespect to a route individually, but may be considered a route in anaggregated system—for example, a mobile data collector may specify arequirement for a route for collecting data based on an input from amonitoring circuit, but only in receiving that input does the mobiledata collector determine what route to take and begin traveling alongthe route. In certain embodiments, a route may be considered a route forsome purposes but not for other purposes—for example possible routesthrough a road system may be considered differently than specific routestaken through from one location to another location. Additionally, incertain embodiments, otherwise similar looking systems may bedifferentiated in determining whether such system are specified withrespect to a location, and/or which types of locations. For example,routes depicted on a map may indicate possible routes or actual routestaken by individuals. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of systems, and any suchsystems may be considered with respect to a route herein, while incertain embodiments a given system may not be considered with respect toa route herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, and/or how tocombine processes and systems from the present disclosure to enhanceoperations of the contemplated system. Certain considerations for theperson of skill in the art, in determining whether a contemplated systemis utilizing a route and/or whether aspects of the present disclosurecan benefit or enhance the contemplated system include, withoutlimitation delivery routes; routes taken through a location; heat mapshowing routes traveled by customers or workers within an environment;determining what resources are deployed to what routes or types oftravel; direct route or multi-stop route, such as from the destinationof the consumer to a specific location or to wherever an event takesplace; a route for a mobile data collector; and the like.

The term future offering (and similar terms) as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, a future offing includes anyoffer of an item or service in the future including, without limitation,a future offer to provide an item or service, a future offer withrespect to a proposed purchase, a future offering made through a forwardmarket platform, a future offering determined by a smart contractcircuit, and the like. Further, a future offering may be a contingentfuture offer or an offer based on conditions resulting on the offerbeing a future offering, such as where the future offer is contingentupon or with the conditions imposed by a predetermined condition (e.g.,a security may be purchased for $1000 at a set future date contingentupon a predetermine state of a market indicator). Certain components maynot be considered a future offering individually, but may be considereda future offering in an aggregated system—for example, an offer for aloan may not be considered a future offering if the offer is notauthorized through a collective agreement amongst a plurality of partiesrelated to the offer, but may be considered a future offer once a votehas been collected and stored through a distributed ledger, such asthrough a blockchain circuit. In certain embodiments, a future offeringmay be considered a future offering for some purposes but not for otherpurposes—for example, a future offering may be contingent upon acondition being meet in the future, and so the future offering may notbe considered a future offer until the condition is met. Additionally,in certain embodiments, otherwise similar looking systems may bedifferentiated in determining whether such system are future offerings,and/or which type of future offerings. For example, two securityofferings may be determined to be offerings to be made at a future time,however, one may have immediate contingences to be met and thus may notbe considered to be a future offering but rather an immediate offeringwith future declarations. Accordingly, the benefits of the presentdisclosure may be applied in a wide variety of systems, and any suchsystems may be considered in association with a future offering herein,while in certain embodiments a given system may not be considered inassociation with a future offering herein. One of skill in the art,having the benefit of the disclosure herein and knowledge about acontemplated system ordinarily available to that person, can readilydetermine which aspects of the present disclosure will benefit aparticular system, and/or how to combine processes and systems from thepresent disclosure to enhance operations of the contemplated system.Certain considerations for the person of skill in the art, indetermining whether a contemplated system is in association with afuture offering and/or whether aspects of the present disclosure canbenefit or enhance the contemplated system include, without limitation aforward offering, a contingent forward offering, a forward offing in aforward market platform (e.g., for creating a future offering orcontingent future offering associated with identifying offering datafrom a platform-operated marketplace or external marketplace); a futureoffering with respect to entering into a smart contract (e.g., byexecuting an indication of a commitment to purchase, attend, orotherwise consume a future offering), and the like.

The term access right (and derivatives or variations) as utilized hereinmay be understood broadly to describe an entitlement to acquire orpossess a property, article, or other thing of value. A contingentaccess right may be conditioned upon a trigger or condition being metbefore such an access right becomes entitled, vested or otherwisedefensible. An access right or contingent access right may also servespecific purposes or be configured for different applications orcontexts, such as, without limitation, loan-related actions or anyservice or offering. Without limitation, notices may be required to beprovided to the owner of a property, article or item of value beforesuch access rights or contingent access rights are exercised. Accessrights and contingent access rights in various forms may be includedwhere discussing a legal action, a delinquent or defaulted loan oragreement, or other circumstances where a lender may be seeking remedy,without limitation. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system, can readily determine the value of such rightsimplemented in an embodiment. While specific examples of access rightsand contingent access rights are described herein for purposes ofillustration, any embodiment benefitting from the disclosures herein,and any considerations understood to one of skill in the art having thebenefit of the disclosures herein, are specifically contemplated withinthe scope of the present disclosure.

The term smart contract (and other forms or variations) as utilizedherein may be understood broadly to describe a method, system, connectedresource or wide area network offering one or more resources useful toassist or perform actions, tasks or things by embodiments disclosedherein. A smart contract may be a set of steps or a process tonegotiate, administrate, restructure or implement an agreement or loanbetween parties. A smart contract may also be implemented as anapplication, website, FTP site, server, appliance or other connectedcomponent or Internet related system that renders resources tonegotiate, administrate, restructure or implement an agreement or loanbetween parties. A smart contract may be a self contained system, or maybe part of a larger system or component that may also be a smartcontract. For example, a smart contract may refer to a loan or anagreement itself, conditions or terms, or may refer to a system toimplement such a loan or agreement. In certain embodiments, a smartcontract circuit or robotic process automation system may incorporate orbe incorporated into automatic robotic process automation system toperform one or more purposes or tasks, whether part of a loan ortransaction process, or otherwise. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system can readily determine the purposes and useof this term as it relates to a smart contract in various forms,embodiments and contexts disclosed herein.

The term allocation of reward (and variations) as utilized herein may beunderstood broadly to describe a thing or consideration allocated orprovided as consideration, or provided for a purpose. The allocation ofrewards can be of a financial type, or non-financial type, withoutlimitation. A specific type of allocation of reward may also serve anumber of different purposes or be configured for different applicationsor contexts, such as, without limitation: a reward event, claims forrewards, monetary rewards, rewards captured as a data set, rewardspoints, and other forms of rewards. Thus an allocation of rewards may beprovided as a consideration within the context of a loan or agreement.Systems may be utilized to allocate rewards. The allocation of rewardsin various forms may be included where discussing a particular behavior,or encouragement of a particular behavior, without limitation. Anallocation of a reward may include an actual dispensation of the award,and/or a recordation of the reward. The allocation of rewards may beperformed by a smart contract circuit or a robotic processing automationsystem. One of skill in the art, having the benefit of the disclosureherein and knowledge ordinarily available about a contemplated system,can readily determine the value of the allocation of rewards in anembodiment. While specific examples of the allocation of rewards aredescribed herein for purposes of illustration, any embodimentbenefitting from the disclosures herein, and any considerationsunderstood to one of skill in the art having the benefit of thedisclosures herein, are specifically contemplated within the scope ofthe present disclosure.

The term satisfaction of parameters or conditions (and otherderivatives, forms, or variations) as utilized herein may be understoodbroadly to describe completion, presence or proof of parameters orconditions that have been met. The term generally may relate to aprocess of determining such satisfaction of parameters or conditions, ormay relate to the completion of such a process with a result, withoutlimitation. Satisfaction may result in a successful outcome of othertriggers or conditions or terms that may come into execution, withoutlimitation. Satisfaction of parameters or conditions may occur in manydifferent contexts of contracts or loans, such as lending, refinancing,consolidation, factoring, brokering, foreclosure, and data processing(e.g. data collection), or combinations thereof, without limitation.Satisfaction of parameters or conditions may be used in the form of anoun (e.g. the satisfaction of the debt repayment), or may be used in averb form to describe the process of determining a result to parametersor conditions. For example, a borrower may have satisfaction ofparameters by making a certain number of payments on time, or may causesatisfaction of a condition allowing access rights to an owner if a loandefaults, without limitation. In certain embodiments, a smart contractor robotic process automation system may perform or determinesatisfaction of parameters or conditions for one or more of the partiesand process appropriate tasks for satisfaction of parameters orconditions. In some cases satisfaction of parameters or conditions bythe smart contract or robotic process automation system may not completeor be successful, and depending upon such outcomes, this may enableautomated action or trigger other conditions or terms. One of skill inthe art, having the benefit of the disclosure herein and knowledgeordinarily available about a contemplated system can readily determinethe purposes and use of this term in various forms, embodiments andcontexts disclosed herein.

The term information (and other forms such as info or informational,without limitation) as utilized herein may be understood broadly in avariety of contexts with respect to an agreement or a loan. The termgenerally may relate to a larger context, such as information regardingan agreement or loan, or may specifically relate to a finite piece ofinformation (e.g. a specific detail of an event that happened on aspecific date). Thus, information may occur in many different contextsof contracts or loans, and may be used in the contexts, withoutlimitation of evidence, transactions, access, and the like. Or, withoutlimitation, information may be used in conjunction with stages of anagreement or transaction, such as lending, refinancing, consolidation,factoring, brokering, foreclosure, and information processing (e.g. dataor information collection), or combinations thereof. For example,information as evidence, transaction, access, etc. may be used in theform of a noun (e.g. the information was acquired from the borrower), ormay refer as a noun to an assortment of informational items (e.g. theinformation about the loan may be found in the smart contract), or maybe used in the form of characterizing as an adjective (e.g. the borrowerwas providing an information submission). For example, a lender maycollect an overdue payment from a borrower through an online payment, ormay have a successful collection of overdue payments acquired through acustomer service telephone call. In certain embodiments, a smartcontract circuit or robotic process automation system may performcollection, administration, calculating, providing, or other tasks forone or more of the parties and process appropriate tasks relating toinformation (e.g. providing notice of an overdue payment). In some casesinformation by the smart contract circuit or robotic process automationsystem may be incomplete, and depending upon such outcomes this mayenable automated action or trigger other conditions or terms. One ofskill in the art, having the benefit of the disclosure herein andknowledge ordinarily available about a contemplated system can readilydetermine the purposes and use of information as evidence, transaction,access, etc. in various forms, embodiments and contexts disclosedherein.

Information may be linked to external information (e.g. externalsources). The term more specifically may relate to the acquisition,parsing, receiving, or other relation to an external origin or source,without limitation. Thus, information linked to external information orsources may be used in conjunction with stages of an agreement ortransaction, such as lending, refinancing, consolidation, factoring,brokering, foreclosure, and information processing (e.g. data orinformation collection), or combinations thereof. For example,information linked to external information may change as the externalinformation changes, such as a borrower's credit score, which is basedon an external source. In certain embodiments, a smart contract circuitor robotic process automation system may perform acquisition,administration, calculating, receiving, updating, providing or othertasks for one or more of the parties and process appropriate tasksrelating to information that is linked to external information. In somecases information that is linked to external information by the smartcontract or robotic process automation system may be incomplete, anddepending upon such outcomes this may enable automated action or triggerother conditions or terms. One of skill in the art, having the benefitof the disclosure herein and knowledge ordinarily available about acontemplated system can readily determine the purposes and use of thisterm in various forms, embodiments and contexts disclosed herein.

Information that is a part of a loan or agreement may be separated frominformation presented in an access location. The term more specificallymay relate to the characterization that information can be apportioned,split, restricted, or otherwise separated from other information withinthe context of a loan or agreement. Thus, information presented orreceived on an access location may not necessarily be the wholeinformation available for a given context. For example, informationprovided to a borrower may be different information received by a lenderfrom an external source, and may be different than information receivedor presented from an access location. In certain embodiments, a smartcontract circuit or robotic process automation system may performseparation of information or other tasks for one or more of the partiesand process appropriate tasks. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system, can readily determine the purposes and useof this term in various forms, embodiments and contexts disclosedherein.

The term encryption of information and control of access (and otherrelated terms) as utilized herein may be understood broadly to describegenerally whether a party or parties may observe or possess certaininformation, actions, events or activities relating to a transaction orloan. Encryption of information may be utilized to prevent a party fromaccessing, observing or receiving information, or may alternatively beused to prevent parties outside the transaction or loan from being ableto access, observe or receive confidential (or other) information.Control of access to information relates to the determination of whethera party is entitled to such access of information. Encryption ofinformation or control of access may occur in many different contexts ofloans, such as lending, refinancing, consolidation, factoring,brokering, foreclosure, administration, negotiating, collecting,procuring, enforcing, and data processing (e.g. data collection), orcombinations thereof, without limitation. An encryption of informationor control of access to information may refer to a single instance, ormay characterize a larger amount of information, actions, events oractivities, without limitation. For example, a borrower or lender mayhave access to information about a loan, but other parties outside theloan or agreement may not be able to access the loan information due toencryption of the information, or a control of access to the loandetails. In certain embodiments, a smart contract circuit or roboticprocess automation system may perform encryption of information orcontrol of access to information for one or more of the parties andprocess appropriate tasks for encryption or control of access ofinformation. One of skill in the art, having the benefit of thedisclosure herein and knowledge ordinarily available about acontemplated system can readily determine the purposes and use of thisterm in various forms, embodiments and contexts disclosed herein.

The term potential access party list (and other related terms) asutilized herein may be understood broadly to describe generally whethera party or parties may observe or possess certain information, actions,events, or activities relating to a transaction or loan. A potentialaccess party list may be utilized to authorize one or more parties toaccess, observe or receive information, or may alternatively be used toprevent parties from being able to do so. A potential access party listinformation relates to the determination of whether a party (either onthe potential access party list or not on the list) is entitled to suchaccess of information. A potential access party list may occur in manydifferent contexts of loans, such as lending, refinancing,consolidation, factoring, brokering, foreclosure, administration,negotiating, collecting, procuring, enforcing and data processing (e.g.data collection), or combinations thereof, without limitation. Apotential access party list may refer to a single instance, or maycharacterize a larger amount of parties or information, actions, eventsor activities, without limitation. For example, a potential access partylist may grant (or deny) access to information about a loan, but otherparties outside potential access party list may not be able to (or maybe granted) access the loan information. In certain embodiments, a smartcontract circuit or robotic process automation system may performadministration or enforcement of a potential access party list for oneor more of the parties and process appropriate tasks for encryption orcontrol of access of information. One of skill in the art, having thebenefit of the disclosure herein and knowledge ordinarily availableabout a contemplated system can readily determine the purposes and useof this term in various forms, embodiments and contexts disclosedherein.

The term offering, making an offer, and similar terms as utilized hereinshould be understood broadly. Without limitation to any other aspect ordescription of the present disclosure, an offering includes any offer ofan item or service including, without limitation, an insurance offering,a security offering, an offer to provide an item or service, an offerwith respect to a proposed purchase, an offering made through a forwardmarket platform, a future offering, a contingent offering, offersrelated to lending (e.g. lending, refinancing, collection,consolidation, factoring, brokering, foreclosure), an offeringdetermined by a smart contract circuit, an offer directed to acustomer/debtor, an offering directed to a provider/lender, a 3rd partyoffer (e.g. regulator, auditor, partial owner, tiered provider) and thelike. Offerings may include physical goods, virtual goods, software,physical services, access rights, entertainment content, accommodations,or many other items, services, solutions, or considerations. In anexample, a third party offer may be to schedule a band instead of justan offer of tickets for sale. Further, an offer may be based onpre-determined conditions or contingencies. Certain components may notbe considered an offering individually, but may be considered anoffering in an aggregated system—for example, an offer for insurance maynot be considered an offering if the offer is not approved by one ormore parties related to the offer, however once approval has beengranted, it may be considered an offer. Accordingly, the benefits of thepresent disclosure may be applied in a wide variety of systems, and anysuch systems may be considered in association with an offering herein,while in certain embodiments a given system may not be considered inassociation with an offering herein. One of skill in the art, having thebenefit of the disclosure herein and knowledge about a contemplatedsystem ordinarily available to that person, can readily determine whichaspects of the present disclosure will benefit a particular system,and/or how to combine processes and systems from the present disclosureto enhance operations of the contemplated system. Certain considerationsfor the person of skill in the art, in determining whether acontemplated system is in association with an offering and/or whetheraspects of the present disclosure can benefit or enhance thecontemplated system include, without limitation the item or servicebeing offered, a contingency related to the offer, a way of tracking ifa contingency or condition has been met, an approval of the offering, anexecution of an exchange of consideration for the offering, and thelike.

The term artificial intelligence (AI) solution should be understoodbroadly. Without limitation to any other aspect of the presentdisclosure, an AI solution includes a coordinated group of AI relatedaspects to perform one or more tasks or operations as set forththroughout the present disclosure. An example AI solution includes oneor more AI components, including any AI components set forth herein,including at least a neural network, an expert system, and/or a machinelearning component. The example AI solution may include as an aspect thetypes of components of the solution, such as a heuristic AI component, amodel based AI component, a neural network of a selected type (e.g.,recursive, convolutional, perceptron, etc.), and/or an AI component ofany type having a selected processing capability (e.g., signalprocessing, frequency component analysis, auditory processing, visualprocessing, speech processing, text recognition, etc.). Withoutlimitation to any other aspect of the present disclosure, a given AIsolution may be formed from the number and type of AI components of theAI solution, the connectivity of the AI components (e.g., to each other,to inputs from a system including or interacting with the AI solution,and/or to outputs to the system including or interacting with the AIsolution). The given AI solution may additionally be formed from theconnection of the AI components to each other within the AI solution,and to boundary elements (e.g., inputs, outputs, stored intermediarydata, etc.) in communication with the AI solution. The given AI solutionmay additionally be formed from a configuration of each of the AIcomponents of the AI solution, where the configuration may includeaspects such as: model calibrations for an AI component; connectivityand/or flow between AI components (e.g., serial and/or parallelcoupling, feedback loops, logic junctions, etc.); the number, selectedinput data, and/or input data processing of inputs to an AI component; adepth and/or complexity of a neural network or other component; atraining data description of an AI component (e.g., training dataparameters such as content, amount of training data, statisticaldescription of valid training data, etc.); and/or a selection and/orhybrid description of a type for an AI component. An AI solutionincludes a selection of AI elements, flow connectivity of those AIelements, and/or configuration of those AI elements.

One of skill in the art, having the benefit of the present disclosure,can readily determine an AI solution for a given system, and/orconfigure operations to perform a selection and/or configurationoperation for an AI solution for a given system. Certain considerationsto determining an AI solution, and/or configuring operations to performa selection and/or configuration operation for an AI solution include,without limitation: an availability of AI components and/or componenttypes for a given implementation; an availability of supportinginfrastructure to implement given AI components (e.g., data input valuesavailable, including data quality, level of service, resolution,sampling rate, etc.; availability of suitable training data for a givenAI solution; availability of expert inputs, such as for an expert systemand/or to develop a model training data set; regulatory and/or policybased considerations including permitted action by the AI solution,requirements for acquisition and/or retention of sensitive data,difficult to obtain data, and/or expensive data); operationalconsiderations for a system including or interacting with the AIsolution, including response time specifications, safety considerations,liability considerations, etc.; available computing resources such asprocessing capability, network communication capability, and/or memorystorage capability (e.g., to support initial data, training data, inputdata such as cached, buffered, or stored input data, iterativeimprovement state data, output data such as cached, buffered, or storedoutput data, and/or intermediate data storage, such as data to supportongoing calculations, historical data, and/or accumulation data); thetypes of tasks to be performed by the AI solution, and the suitabilityof AI components for those tasks, sensitivity of AI componentsperforming the tasks (e.g., variability of the output space relative toa disturbance size of the input space); the interactions of AIcomponents within the entire AI solution (e.g., a low capabilityrationality AI component may be coupled with a higher capability AIcomponent that may provide high sensitivity and/or unbounded response toinputs); and/or model implementation considerations (e.g., requirementsto re-calibrate, aging constraints of a model, etc.).

A selected and/or configured AI solution may be utilized with any of thesystems, procedures, and/or aspects of embodiments as set forththroughout the present disclosure. For example, a system utilizing anexpert system may include the expert system as all or a part of aselected, configured AI solution. In another example, a system utilizinga neural network, and/or a combination of neural networks, may includethe neural network(s) as all or a part of a selected, configured AIsolution. The described aspects of an AI solution, including theselection and configuration of the AI solution, are non-limitingillustrations.

Referring to FIG. 1, an embodiment 100 of a financial, transactional andmarketplace enablement system is illustrated wherein a lendingenablement platform 100 is enabled and wherein a platform-orientedmarketplace 132 may comprise a lending application 144. The lendingenablement platform 100 may include a set of systems, applications,processes, modules, services, layers, devices, components, machines,products, sub-systems, interfaces, connections, and other elements(collectively referred in the alternative, except where contextindicates otherwise, as the “platform,” the “lending platform,” the“system,” and the like) working in coordination (such as by dataintegration and organization in a services oriented architecture) toenable intelligent management of a set of entities 198 that may occur,operate, transact or the like within, or own, operate, support orenable, one or more applications, services, solutions, programs or thelike of the lending application 144 or external marketplaces 188 thatinvolve lending transactions or lending-related entities, or that mayotherwise be part of, integrated with, linked to, or operated on by thelending enablement platform 100. References to a set of services hereinshould be understood, except where context indicates otherwise, theseand other various systems, applications, processes, modules, services,layers, devices, components, machines, products, sub-systems,interfaces, connections, and other types of elements. FIG. 1 includes amanagement application platform 126 comprising a lending application144, adaptive intelligence systems 158, monitoring systems 164, datacollection system 166, data storage systems 186, all interfacing withdata handling layers 168. FIG. 1 also depicts the disclosed systemshaving process and application outputs and outcomes 151 and incommunication with entities 198. Components of the lending application144 may include underwriting 103, risk management 122, analytics 130,pricing 131, tax 124, crowdsourcing system 120, smart contract 134,blockchain 136, lending model 108, trust and custody 150, platformmarketplace 132, fraud 138, regulatory 142, payments 146, and security148. A set may include multiple members or a single member. The adaptiveintelligence systems 158 may include opportunity miners 153, roboticprocess automation (RPA) 154, artificial intelligence 156, artificialintelligence store 157, and clustering 104. The monitoring systems 164and data collection system 166 may include software interactionobservation 160, functional imaging 161, and physical processobservation 162. The data storage system 186 may include access data170, pricing data 178, asset and facility data 172, claims data 180,worker data 174, accounting data 182, event data 176, and underwritingdata 184. Entities 198 may include external marketplaces 188, collateral102, facilities 190, collaborative robotics 193, workers 194,wearable/portable devices 195, processes 196, and machines 197. As withother embodiments, the lending enablement platform 100 may have variousdata handling layers, with components, modules, systems, services,components, functions and other elements described in connection withother embodiments described throughout this disclosure and the documentsincorporated herein by reference. This may include various adaptiveintelligent systems 158, monitoring systems 164, data collection systems166, and data storage systems 186, as well as a set of interfaces 187of, to, and/or among each of those systems and/or the various otherelements of the lending enablement platform 100. In embodiments theinterfaces 187 may include application programming interfaces 112; dataintegration technologies for extracting, transforming, cleansing,normalizing, deduplicating, loading and the like as data is moved amongvarious services using various protocols and formats (collectivelyreferred to as ETL systems 114); and various ports, portals, connectors,gateways, wired connections, sockets, virtual private networks,containers, secure channels and other connections configured amongelements on a one-to-one, one-to-many, or many-to-one basis, such as inunicast, broadcast and multi-cast transmission (collectively referred toas ports 118). Interfaces 187 may include, be enabled by, integratewith, or interface with a real time operating system (RTOS) 110, such asthe FreeRTOS™ operating system, that has a deterministic executionpattern in which a user may define an execution pattern, such as basedon assignment of a priority to each thread of execution. An instance ofthe RTOS 110 may be embedded, such as on a microcontroller of anInternet of Things device, such as one used to monitor various entities198. The RTOS 110 may provide real-time scheduling (such as schedulingof data transmissions to monitoring systems 164 and data collectionsystems 166, scheduling of inter-task communication among variousservice elements, and other timing and synchronization elements). Inembodiments the interfaces 187 may use or include a set of librariesthat enable secure connection between small, low-power edge devices,such as Internet of Things devices used to monitor various entities 198,and various cloud-deployed services of the lending enablement platform100, as well as a set of edge devices and the systems that enable them,such as ones running local data processing and computing systems such asAWS IoT Greengrass™ and/or AWS Lambda™ functions, such as to allow localcalculation, configuration of data communication, execution of machinelearning models (such as for prediction or classification),synchronization of devices or device data, and communication amongdevices and services. This may include use of local device resourcessuch as serial ports, GPUs, sensors and cameras. In embodiments, datamay be encrypted for secure end-to-end communication.

In the context of a lending enablement platform 100 and set of lendingapplication 144, various entities 198 may include any of the widevariety of assets, systems, devices, machines, facilities, individualsor other entities mentioned throughout this disclosure or in thedocuments incorporated herein by reference, such as, without limitation:machines 197 and their components (e.g., machines that are the subjectof a loan or collateral for a loan, such as various vehicles andequipment, as well as machines used to conduct lending transactions,such as automated teller machines, point of sale machines, vendingmachines, kiosks, smart-card-enabled machines, and many others,including ones used to enable microloans, payday loans and others);financial and transactional processes 196 (such as lending processes,inspection processes, collateral tracking processes, valuationprocesses, credit checking processes, creditworthiness processes,syndication processes, interest rate-setting processes, softwareprocesses (including applications, programs, services, and others),production processes, collection processes, banking processes (e.g.,lending processes, underwriting processes, investing processes, and manyothers), financial service processes, diagnostic processes, securityprocesses, safety processes, assessment processes, payment processes,valuation processes, issuance processes, factoring processes,consolidation processes, syndication processes, collection processes,foreclosure processes, title transfer processes, title verificationprocesses, collateral monitoring processes, and many others); wearableand portable devices 195 (such as mobile phones, tablets, dedicatedportable devices for financial applications, data collectors (includingmobile data collectors), sensor-based devices, watches, glasses,hearables, head-worn devices, clothing-integrated devices, arm bands,bracelets, neck-worn devices, AR/VR devices, headphones, and manyothers); workers 194 (such as banking workers, loan officers, financialservice personnel, managers, inspectors, brokers (e.g., mortgagebrokers), attorneys, underwriters, regulators, assessors, appraisers,process supervisors, security personnel, safety personnel and manyothers); robotic systems 192 (e.g., physical robots, collaborativerobots (e.g., “cobots”), software bots and others); and facilities 190(such as banking facilities, inventory warehousing facilities,factories, homes, buildings, storage facilities (such as forloan-related collateral, property that is the subject of a loan,inventory (such as related to loans on inventory), personal property,components, packaging materials, goods, products, machinery, equipment,and other items), banking facilities (such as for commercial banking,investing, consumer banking, lending and many other banking activities)and others. In embodiments, various entities 198 may include externalmarketplaces 188, such as financial, commodities, e-commerce,advertising, and other external marketplaces 188 (including current andfutures markets), such as ones within which transactions occur invarious goods and services, such that monitoring of the externalmarketplaces 188 and various entities 198 within them may providelending-relevant information, such as with respect to the price or valueof items, the liquidity of items, the characteristics of items, the rateof depreciation of items, or the like. For example, for various entitiesthat may comprise collateral 102 or assets for asset-backed lending, amonitoring system 164 may monitor not only the collateral 102 or assets,such as by cameras, sensors, or other monitoring systems 164, but mayalso collect data, such as via data collection systems 166 of varioustypes, with respect to the value, price, or other condition of thecollateral 102 or assets, such as by determining market conditions forcollateral 102 or assets that are in similar condition, of similar age,having similar specifications, having similar location, or the like. Inembodiments, an adaptive intelligent system 158 may include a clusteringcircuit 104, such as one that groups or clusters various entities 198,including collateral 102, parties, assets, or the like by similarity ofattributes, such as a k-means clustering system, self-organizing mapsystem, or other system as described herein and in the documentsincorporated herein by reference. The clustering system may organizecollections of collateral, collections of assets, collections ofparties, and collections of loans, for example, such that they may bemonitored and analyzed based on common attributes, such as to enableperformance of a subset of transactions to be used to predictperformance of others, which in turn may be used for underwriting 103,pricing 131, fraud prevention applications 138, or other applications,including any of the services, solutions, or applications described inconnection with FIG. 1 and FIG. 2 or elsewhere throughout thisdisclosure or the documents incorporated herein by reference. Inembodiments condition information about collateral 102 or assets iscontinuously monitored by a monitoring system 164, such as a set ofsensors on the collateral 102 or assets, a set of sensors or cameras inthe environment of the collateral 102 or assets, or the like, and marketinformation is collected in real time by a data collection system 166,such that the condition and market information may be time-aligned andused as a basis for real time estimation of the value of the collateralor assets and forward prediction of the future value of the collateralor assets. Present and predicted value for the collateral 102 or assetsmay be based on a model, which may be accessed and used, such as in asmart contract, to enable automated, or machine-assisted lending on thecollateral or assets, such as the underwriting or offering of amicroloan on the collateral 102 or assets. Aggregation of data for a setof collateral 102 or set of assets, such as a collection or fleet ofcollateral 102 or fleet of assets owned by an entity 198 may allow realtime portfolio valuation and larger scale lending, including via smartcontracts that automatically adjust interest rates and other terms andconditions based on the individual or aggregated value of collateral 102or assets based on real time condition monitoring and real-time marketdata collection and integration. Transactions, party information,transfers of title, changes in terms and conditions, and otherinformation may be stored in a blockchain 136, including loantransactions and information (such as condition information forcollateral 102 or assets and marketplace data) about the collateral 102or assets. The smart contract may be configured to require a party toconfirm condition information and/or market value information, such asby representations and warranties that are supported or verified by themonitoring systems 164 (which may flag fraud in a fraud preventionapplication 138). A lending model 108 may be used to value collateral102 or assets, to determine eligibility for lending based on thecondition and/or value of collateral 102 or assets, to set pricing(e.g., interest rates), to adjust terms and conditions, and the like.The lending model 108 may be created by a set of experts, such as usingcalculated analytics 130 on past lending transactions. The lending model108 may be populated by data from monitoring systems 164 and datacollection systems 166, may pull data from data storage systems 186, andthe like. The lending model 108 may be used to configure parameters of asmart contract, such that smart contract terms and conditionsautomatically adjust based on adjustments in the lending model 108. Thelending model 108 may be configured to be improved by artificialintelligence 156, such as by training it on a set of outcomes, such asoutcomes from lending transactions (e.g., payment outcomes, defaultoutcomes, performance outcomes, and the like), outcomes on collateral102 or assets (such as prices or value patterns of collateral or assetsover time), outcomes on entities (such as defaults, foreclosures,performance results, on time payments, late payments, bankruptcies, andthe like), and others. Training may be used to adjust and improve modelparameters and performance, including for classification of collateralor assets (such as automatic classification of type and/or condition,such as using vision-based classification from camera-based monitoringsystems 164), prediction of value of collateral 102 or assets,prediction of defaults, prediction of performance, and the like. Inembodiments, configuration or handling of smart contracts for lending oncollateral 102 or assets may be learned and automated in a roboticprocess automation (RPA) system 154, such as by training the RPA system154 to create smart contracts, configure parameters of smart contracts,confirm title to collateral 102 or assets, set terms and conditions ofsmart contracts, initiate security interests on collateral 102 for smartcontracts, monitor status or performance of smart contracts, terminateor initiate termination for default of smart contracts, close smartcontracts, foreclose on collateral 102 or assets, transfer title, or thelike, such as by using monitoring systems 164 to monitor expert entities198, such as human managers, as they undertake a training set of similartasks and actions in the creation, configuration, title confirmation,initiation of security interests, monitoring, termination, closing,foreclosing, and the like for a training set of smart contracts. Once anRPA system 154 is trained, it may efficiently create the ability toprovide lending at scale across a wide range of entities and assets thatmay serve as collateral 102, that may provide guarantees or security, orthe like, thereby making loans more readily available for a wider rangeof situations, entities 198, and collateral 102. The RPA system 154 mayitself be improved by artificial intelligence 156, such as bycontinuously adjusting model parameters, weights, configurations, or thelike based on outcomes, such as loan performance outcomes, collateralvaluation outcomes, default outcomes, closing rate outcomes, interestrate outcomes, yield outcomes, return-on-investment outcomes, or others.Smart contracts may include or be used for direct lending, syndicatedlending, and secondary lending contracts, individual loans or aggregatedtranches of loans, and the like.

In embodiments, the lending application 144 of the managementapplication platform 126 may, in various optional embodiments, include,integrate with, or interact with (such as within other embodiments ofthe lending enablement platform) a set of applications, such as ones bywhich a lender, a borrower, a guarantor, an operator or owner of atransactional or financial entity, or other user, may manage, monitor,control, analyze, or otherwise interact with one or more elementsrelated to a loan, such as an entity 198 that is a party to a loan, thesubject of a loan, the collateral for a loan, or otherwise relevant tothe loan. This may include any of the elements noted above in connectionwith FIG. 1. The set of applications may include a lending application144 (such as, without limitation, for personal lending, commerciallending, collateralized lending, microlending, peer-to-peer lending,insurance-related lending, asset-backed lending, secured debt lending,corporate debt lending, student loans, subsidized loans, mortgagelending, municipal lending, sovereign debt, automotive lending, pay dayloans, loans against receivables, factoring transactions, loans againstguaranteed or assured payments (such as tax refunds, annuities, and thelike), and many others). The lending application 144 may include,integrate with, or link with one or more of any of a wide range of othertypes of applications that may be relevant to lending, such as aninvestment application (such as, without limitation, for investment intranches of loans, corporate debt, bonds, syndicated loans, municipaldebt, sovereign debt, or other types of debt-related securities); anasset management application (such as, without limitation, for managingassets that may be the subject of a loan, the collateral for a loan,assets that back a loan, the collateral for a loan guarantee, orevidence of creditworthiness, assets related to a bond, investmentassets, real property, fixtures, personal property, real estate,equipment, intellectual property, vehicles, and other assets); a riskmanagement solution 122 (such as, without limitation, for managing riskor liability with respect to subject of a loan, a party to a loan, or anactivity relevant to the performance of a loan, such as a product, anasset, a person, a home, a vehicle, an item of equipment, a component,an information technology system, a security system, a security event, acybersecurity system, an item of property, a health condition,mortality, fire, flood, weather, disability, business interruption,injury, damage to property, damage to a business, breach of a contract,and others); a marketing application 202 (such as, without limitation,an application for marketing a loan or a tranche of loans, a customerrelationship management application for lending, a search engineoptimization application for attracting relevant parties, a salesmanagement application, an advertising network application, a behavioraltracking application, a marketing analytics application, alocation-based product or service targeting application, a collaborativefiltering application, a recommendation engine for loan-related productor service, and others); a trading application (such as, withoutlimitation, an application for trading a loan, a tranche of loans, aportion of a loan, a loan-related interest, or the like, such as abuying application, a selling application, a bidding application, anauction application, a reverse auction application, a bid/ask matchingapplication, or others); a tax application 262 (such as, withoutlimitation, for managing, calculating, reporting, optimizing, orotherwise handling data, events, workflows, or other factors relating toa tax-related impact of a loan); a fraud prevention application 138(such as, without limitation, one or more of an identity verificationapplication, a biometric identity validation application, atransactional pattern-based fraud detection application, alocation-based fraud detection application, a user behavior-based frauddetection application, a network address-based fraud detectionapplication, a black list application, a white list application, acontent inspection-based fraud detection application, or other frauddetection application; a security application, solution or service(referred to herein as a security application 148, such as, withoutlimitation, any of the fraud prevention applications 138 noted above, aswell as a physical security system (such as for an access control system(such as using biometric access controls, fingerprinting, retinalscanning, passwords, and other access controls), a safe, a vault, acage, a safe room, or the like), a monitoring system (such as usingcameras, motion sensors, infrared sensors and other sensors), a cybersecurity system (such as for virus detection and remediation, intrusiondetection and remediation, spam detection and remediation, phishingdetection and remediation, social engineering detection and remediation,cyberattack detection and remediation, packet inspection, trafficinspection, DNS attack remediation and detection, and others) or othersecurity application); an underwriting application 103 (such as, withoutlimitation, for underwriting any loan, guarantee, or other loan-relatedtransaction or obligation, including any application for detecting,characterizing or predicting the likelihood and/or scope of a risk,including underwriting based on any of the data sources, events orentities noted throughout this disclosure or the documents incorporatedherein by reference); a blockchain application for storing informationas a blockchain 136 (such as, without limitation, a distributed ledgercapturing a series of transactions, such as debits or credits, purchasesor sales, exchanges of in kind consideration, smart contract events, orthe like, a cryptocurrency application, or other blockchain-basedapplication); a real estate application (such as, without limitation, areal estate brokerage application, a real estate valuation application,a real estate mortgage or lending application, a real estate assessmentapplication, or other); a regulatory and/or compliance solution 142(such as, without limitation, an application for regulating the termsand conditions of a loan, such as the permitted parties, the permittedcollateral, the permitted terms for repayment, the permitted interestrates, the required disclosures, the required underwriting process,conditions for syndication, and many others); a platform-orientedmarketplace 500 such as marketplace application, solution or service(referred to as a marketplace application, such as, without limitation,a loan syndication marketplace, a blockchain-based marketplace, acryptocurrency marketplace, a token-based marketplace, a marketplace foritems used as collateral, or other marketplace); a warranty or guaranteeapplication (such as, without limitation, an application for a warrantyor guarantee with respect to an item that is the subject of a loan,collateral for a loan, or the like, such as a product, a service, anoffering, a solution, a physical product, software, a level of service,quality of service, a financial instrument, a debt, an item ofcollateral, performance of a service, or other item); an analystapplication 130 (such as, without limitation, an analytic applicationwith respect to any of the data types, applications, events, workflows,or entities mentioned throughout this disclosure or the documentsincorporated by reference herein, such as a big data application, a userbehavior application, a prediction application, a classificationapplication, a dashboard, a pattern recognition application, aneconometric application, a financial yield application, a return oninvestment application, a scenario planning application, a decisionsupport application, and many others); a pricing application 131 (suchas, without limitation, for pricing of interest rates and other termsand conditions for a loan). Thus, the management application platform126 may host and enable interaction among a wide range of disparateapplications (such term including the above-referenced and otherfinancial or transactional applications, services, solutions, and thelike), such that by virtue of shared microservices, shared datainfrastructure, and shared intelligence, any pair or larger combinationor permutation of such services may be improved relative to an isolatedapplication of the same type.

In embodiments the data collection systems 166 and the monitoringsystems 164 may monitor one or more events related to a loan, debt,bond, factoring agreement, or other lending transaction, such as eventsrelated to requesting a loan, offering a loan, accepting a loan,providing underwriting information for a loan, providing a creditreport, deferring a required payment, setting an interest rate for aloan, deferring a payment requirement, identifying collateral or assetsfor a loan, validating title for collateral or security for a loan,recording a change in title of property, assessing the value ofcollateral or security for a loan, inspecting property that is involvedin a loan, a change in condition of an entity relevant to a loan, achange in value of an entity that is relevant to a loan, a change in jobstatus of a borrower, a change in financial rating of a lender, a changein financial value of an item offered as a security, providing insurancefor a loan, providing evidence of insurance for property related to aloan, providing evidence of eligibility for a loan, identifying securityfor a loan, underwriting a loan, making a payment on a loan, defaultingon a loan, calling a loan, closing a loan, setting terms and conditionsfor a loan, foreclosing on property subject to a loan, and modifyingterms and conditions for a loan.

Microservices Lending Platform with Data Collection Services, Blockchainand Smart Contracts

In embodiments, provided herein is a platform, consisting of variousservices, components, modules, programs, systems, devices, algorithms,and other elements, for lending. In embodiments, the platform or systemincludes a set of microservices having a set of application programminginterfaces that facilitate connection among the microservices and to themicroservices by programs that are external to the platform, wherein themicroservices include (a) a multi-modal set of data collection servicesthat collect information about and monitor entities related to a lendingtransaction; (b) a set of blockchain services for maintaining a securehistorical ledger of events related to a loan, the blockchain serviceshaving access control features that govern access by a set of partiesinvolved in a loan; (c) a set of application programming interfaces,data integration services, data processing workflows and user interfacesfor handling loan-related events and loan-related activities; and (d) aset of smart contract services for specifying terms and conditions ofsmart contracts that govern at least one of loan terms and conditions,loan-related events and loan-related activities.

In embodiments the entities relevant to lending include a set ofentities among lenders, borrowers, guarantors, equipment, goods,systems, fixtures, buildings, storage facilities, and items ofcollateral.

In embodiments collateral items are monitored and the collateral itemsare selected from among a vehicle, a ship, a plane, a building, a home,real estate property, undeveloped land, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

In embodiments the multi-modal set of data collection services includeservices selected from among a set of Internet of Things systems thatmonitor the entities, a set of cameras that monitor the entities, a setof software services that pull information related to the entities frompublicly available information sites, a set of mobile devices thatreport on information related to the entities, a set of wearable devicesworn by human entities, a set of user interfaces by which entitiesprovide information about the entities and a set of crowdsourcingservices configured to solicit and report information related to theentities.

In embodiments the events related to a loan are selected from requestinga loan, offering a loan, accepting a loan, providing underwritinginformation for a loan, providing a credit report, deferring a requiredpayment, setting an interest rate for a loan, deferring a paymentrequirement, identifying collateral for a loan, validating title forcollateral or security for a loan, recording a change in title ofproperty, assessing the value of collateral or security for a loan,inspecting property that is involved in a loan, a change in condition ofan entity relevant to a loan, a change in value of an entity that isrelevant to a loan, a change in job status of a borrower, a change infinancial rating of a lender, a change in financial value of an itemoffered as a security, providing insurance for a loan, providingevidence of insurance for property related to a loan, providing evidenceof eligibility for a loan, identifying security for a loan, underwritinga loan, making a payment on a loan, defaulting on a loan, calling aloan, closing a loan, setting terms and conditions for a loan,foreclosing on property subject to a loan, and modifying terms andconditions for a loan.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

In embodiments a set of parties to the loan is selected from among aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official,and an accountant.

In embodiments loan-related activities include activities selected fromthe set of finding parties interested in participating in a loantransaction, an application for a loan, underwriting a loan, forming alegal contract for a loan, monitoring performance of a loan, makingpayments on a loan, restructuring or amending a loan, settling a loan,monitoring collateral for a loan, forming a syndicate for a loan,foreclosing on a loan, and closing a loan transaction.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments smart contract services configure at least one smartcontract to automatically undertake a loan-related action based on basedon information collected by the multi-modal set of data collectionservices.

In embodiments the loan-related action is selected from among offering aloan, accepting a loan, underwriting a loan, setting an interest ratefor a loan, deferring a payment requirement, modifying an interest ratefor a loan, validating title for collateral, recording a change intitle, assessing the value of collateral, initiating inspection ofcollateral, calling a loan, closing a loan, setting terms and conditionsfor a loan, providing notices required to be provided to a borrower,foreclosing on property subject to a loan, and modifying terms andconditions for a loan.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of items of collateral and undertakes anaction related to a loan to which the collateral is subject.

In embodiments the loan-related action is selected from among offering aloan, accepting a loan, underwriting a loan, setting an interest ratefor a loan, deferring a payment requirement, modifying an interest ratefor a loan, validating title for collateral, recording a change intitle, assessing the value of collateral, initiating inspection ofcollateral, calling a loan, closing a loan, setting terms and conditionsfor a loan, providing notices required to be provided to a borrower,foreclosing on property subject to a loan, and modifying terms andconditions for a loan.

Referring to FIG. 2, additional applications, solutions, programs,systems, services and the like that may be present in a lendingapplication 144 are depicted, which may be interchangeably included inthe management application platform 126 with other elements noted inconnection with FIG. 1 and elsewhere throughout this disclosure and thedocuments incorporated herein by reference. Also depicted are additionalentities 198, which should be understood to be interchangeable with theother entities 198 described in connection with various embodimentsdescribed herein. In addition to elements already noted above, thelending application 144 may include a set of applications, solutions,programs, systems, services and the like that include one or more of asocial network analytics application 204 that may find and analyzeinformation about various entities 198 as depicted in one or more socialnetworks (such as, without limitation, information about parties,behavior of parties, conditions of assets, events relating to parties orassets, conditions of facilities, location of collateral 102 or assets,and the like), such as by allowing a user to configure queries that maybe initiated and managed across a set of social network sites using datacollection systems 166 and monitoring systems 164; a crowdsourcingsolution 250; a loan management solution 149 (such as for managing orresponding to one or more events related to a loan (such eventsincluding, among others, requests for a loan, offering a loan, acceptinga loan, providing underwriting information for a loan, providing acredit report, deferring a required payment, setting an interest ratefor a loan, deferring a payment requirement, identifying collateral fora loan, validating title for collateral or security for a loan,recording a change in title of property, assessing the value ofcollateral or security for a loan, inspecting property that is involvedin a loan, a change in condition of an entity relevant to a loan, achange in value of an entity that is relevant to a loan, a change in jobstatus of a borrower, a change in financial rating of a lender, a changein financial value of an item offered as a security, providing insurancefor a loan, providing evidence of insurance for property related to aloan, providing evidence of eligibility for a loan, identifying securityfor a loan, underwriting a loan, making a payment on a loan, defaultingon a loan, calling a loan, closing a loan, setting terms and conditionsfor a loan, foreclosing on property subject to a loan, and modifyingterms and conditions for a loan) for setting terms and conditions for aloan (such as a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default), or managing loan-related activities (such as, withoutlimitation, finding parties interested in participating in a loantransaction, handling an application for a loan, underwriting a loan,forming a legal contract for a loan, monitoring performance of a loan,making payments on a loan, restructuring or amending a loan, settling aloan, monitoring collateral for a loan, forming a syndicate for a loan,foreclosing on a loan, collecting on a loan, consolidating a set ofloans, analyzing performance of a loan, handling a default of a loan,transferring title of assets or collateral, and closing a loantransaction)); a rating solution 2102 (such as for rating an entity 198(such as a party 210, collateral 102, asset 218 or the like), such asinvolving rating of creditworthiness, financial health, physicalcondition, status, value, presence or absence of defects, quality, orother attribute); regulatory and/or compliance solution 142 (such as forenabling specification, application and/or monitoring of one or morepolicies, rules, regulations, procedures, protocols, processes, or thelike, such as ones that relate to terms and conditions of loantransactions, steps required in forming lending transactions, stepsrequired in performing lending transactions, steps required with respectto security or collateral, steps required for underwriting, stepsrequired for setting prices, interest rates, or the like, steps requiredto provide required legal disclosures and notices (e.g., presentingannualized percentage rates) and others); a custodial solution or set ofcustodial solution 1802 (such as for taking custody of a set of assets218, collateral 102, or the like (including cryptocurrencies, currency,securities, stocks, bonds, agreements evidencing ownership interests,and many other items), such as on behalf of a party 210, client, orother entity 198 that needs assistance in maintaining security of theitems, or in order to provide security, backing, or a guarantee for anobligation, such as one involved in a lending transaction); a loanmarketing solution 2002 (such as for enabling a lender to marketavailability of a loan to a set of prospective borrowers, to target aset of borrowers who are appropriate for a type of transaction, toconfigure marketing or promotional messages (including placement andtiming of the message), to configure advertisement and promotionalchannels for lending transactions, to configure promotional or loyaltyprogram parameters, and many others); a brokering solution 244 (such asfor brokering a set of loan transactions among a set of parties, such asa mortgage loan), which may allow a user to configure a set ofpreferences, profiles, parameters, or the like to find a set ofprospective counterparties to a lending transaction; a bond managementsolution 234 such as for managing, reporting on, syndicating,consolidating, or otherwise handling a set of bonds (such as municipalbonds, corporate bonds, performance bonds, and others); a guaranteeand/or security monitoring solution 230, such as for monitoring,classifying, predicting, or otherwise handling the reliability, quality,status, health condition, financial condition, physical condition orother information about a guarantee, a guarantor, a set of collateralsupporting a guarantee, a set of assets backing a guarantee, or thelike; a negotiation solution 232, such as for assisting, monitoring,reporting on, facilitating and/or automating negotiation of a set ofterms and conditions for a lending transaction (such as, withoutlimitation, a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclosure condition, a default condition, and aconsequence of default), which may include a set of user interfaces forconfiguration of parameters, profiles, preferences, or the like fornegotiation, such as ones that use or are informed by the lending model108 and ones that use, are informed by, or that are automated by or withthe assistance of a set of artificial intelligence 156 services andsystems, by robotic process automation (RPA) 154, or other adaptiveintelligent systems 158; a collection solution 238 for collecting on aloan, which may optionally use, be informed by, or be automated by orwith the assistance of a set of artificial intelligence 156 services andsystems, by robotic process automation 154, or other adaptiveintelligent systems 158, such as based on monitoring the status orcondition of various entities 198 with the monitoring systems 164 anddata collection systems 166 in order to trigger collection, such as whenone or more covenants has not been met, when collateral is in poorcondition, when financial health of party is below a threshold, or thelike; a consolidation solution 240 for consolidating a set of loans,such as using a lending model 108 that is configured for modeling aconsolidated set of loans and such as using or being automated by one ormore adaptive intelligent systems 158; a custodial solution 258; afactoring solution 242, such as for monitoring, managing, automating orotherwise handling a set of factoring transactions, such as using alending model 108 that is configured for modeling factoring transactionsand such as using or being automated by one or more adaptive intelligentsystems 158; a debt restructuring solution 228, such as forrestructuring a set of loans or debt, such as using a lending model 108that is configured for modeling alternative scenarios for restructuringa set of loans or debt and such as using or being automated by one ormore adaptive intelligent systems 158; and/or an interest rateautomation solution 224, such as for setting or configuring a set ofrules or a model for a set of interest rates for a set of lendingtransactions or for automating interest rate setting based oninformation collected by data collection systems 166 or monitoringsystems 164 (such as information about conditions, status, health,location, geolocation, storage condition, or other relevant informationabout any of the entities 198), which may set interest rates orfacilitate setting of interest rates for a set of loans, such as using alending model 108 that is configured for modeling interest ratescenarios for a set of loans and such as using or being automated by oneor more of the adaptive intelligent systems 158. As with the solutionsreferenced in connection with FIG. 1, the various solutions may sharethe adaptive intelligent systems 158, the monitoring systems 164, thedata collection systems 166 and the data storage systems 186, such as bybeing integrated into the lending enablement platform 100 in amicroservices architecture having various appropriate data integrationservices, APIs 112, and interfaces.

As with the entities 198 described in connection with FIG. 2, entities198 may further include a range of entities that are involved withloans, debt transactions, bonds, factoring agreements, and other lendingtransactions, such as: collateral 102 and assets 218 that are used tosecure, guarantee, or back a payment obligation (such as vehicles,ships, planes, buildings, homes, real estate, undeveloped land, farms,crops, facilities 190 (such as municipal facilities, factories,warehouses, storage facilities, treatment facilities, plants, andothers), systems, a set of inventory, commodities, securities,currencies, tokens of value, tickets, cryptocurrencies, consumables,edibles, beverages, precious metals, jewelry, gemstones, intellectualproperty, intellectual property rights, contractual rights, legalrights, antiques, fixtures, equipment, furniture, tools, machinery andpersonal property); a set of parties 210 (such as one or more of aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, a bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, an agent, an attorney, a valuation professional, agovernment official, and/or an accountant); a set of lending agreements220 (such as loans, bonds 212, lending agreements, corporate debtagreements, subsidized loan agreements, factoring agreements,consolidation agreements, syndication agreements, guarantee agreements,underwriting agreements, and others, which may include a set of termsand conditions that may be searched, collected, monitored, modified orotherwise handled by the lending enablement platform 100, such asinterest rates, payment schedules, payment amounts, principal amounts,representations and warranties, indemnities, covenants, and other termsand conditions); a set of guarantees 214 (such as provided by personalguarantors, corporate guarantors, government guarantors, municipalguarantors and others to secure or back a payment obligation or otherobligation of a lending agreement 220); a set of performance activities222 (such as making payments of principal and/or interest, maintainingrequired insurance, maintaining title, satisfying covenants, maintainingcondition of collateral 102 or assets 218, conducting business asrequired by an agreement; and many others); and devices 252 (such asInternet of Things devices that may be disposed on or in goods,equipment or other items, such as ones that are collateral 102 or assets218 used to back a payment obligation or to satisfy a covenant or otherrequirement, or that may be disposed on or in packaging for goods, aswell as ones disposed in facilities 190 or other environments whereentities 198 may be located). In embodiments a lending agreement 220 maybe for a bond, a factoring agreement, a syndication agreement, aconsolidation agreement, a settlement agreement, or a loan, such as oneor more of an auto loan, an inventory loan, a capital equipment loan, abond for performance, a capital improvement loan, a building loan, aloan backed by an account receivable, an invoice finance arrangement, afactoring arrangement, a pay day loan, a refund anticipation loan, astudent loan, a syndicated loan, a title loan, a home loan, a venturedebt loan, a loan of intellectual property, a loan of a contractualclaim, a working capital loan, a small business loan, a farm loan, amunicipal bond, and a subsidized loan.

As noted elsewhere herein and in documents incorporated by reference,artificial intelligence (such as any of the techniques or systemsdescribed throughout this disclosure) in connection with varioustransactional and marketplace entities 198 and related processes andapplications may be used to facilitate, among other things: (a) theoptimization, automation and/or control of various functions, workflows,applications, features, resource utilization and other factors, (b)recognition or diagnosis of various states, entities, patterns, events,contexts, behaviors, or other elements; and/or (c) the forecasting ofvarious states, events, contexts or other factors. As artificialintelligence improves, a large array of domain-specific and/or generalartificial intelligence systems have become available and are likely tocontinue to proliferate. As developers seek solutions to domain-specificproblems, such as ones relevant to entities 198 and applications of theplatform 126 described throughout this disclosure they face challengesin selecting artificial intelligence models (such as what set of neuralnetworks, machine learning systems, expert systems, or the like toselect) and in discovering and selecting what inputs may enableeffective and efficient use of artificial intelligence for a givenproblem. As noted above, opportunity miners 153 may assist with thediscovery of opportunities for increased automation and intelligence;however, once opportunities are discovered, selection and configurationof an artificial intelligence solution still presents a significantchallenge, one that is likely to continue to grow as artificialintelligence solutions proliferate.

One set of solutions to these challenges is an artificial intelligencestore 157 that is configured to enable collection, organization,recommendation and presentation of relevant sets of artificialintelligence systems based on one or more attributes of a domain and/ora domain-related problem. In embodiments, an artificial intelligencestore 157 may include a set of interfaces to artificial intelligencesystems, such as enabling the download of relevant artificialintelligence applications, establishment of links or other connectionsto artificial intelligence systems (such as links to cloud-deployedartificial intelligence systems via APIs, ports, connectors, or otherinterfaces) and the like. The artificial intelligence store 157 mayinclude descriptive content with respect to each of a variety ofartificial intelligence systems, such as metadata or other descriptivematerial indicating suitability of a system for solving particular typesof problems (e.g., forecasting, NLP, image recognition, patternrecognition, motion detection, route optimization, or many others)and/or for operating on domain-specific inputs, data or other entities.In embodiments, the artificial intelligence store 157 may be organizedby category, such as domain, input types, processing types, outputtypes, computational requirements and capabilities, cost, energy usage,and other factors. In embodiments, an interface to the application store157 may take input from a developer and/or from the platform (such asfrom an opportunity miner 153) that indicates one or more attributes ofa problem that may be addressed through artificial intelligence and mayprovide a set of recommendations, such as via an artificial intelligenceattribute search engine, for a subset of artificial intelligencesolutions that may represent favorable candidates based on thedeveloper's domain-specific problem.

In embodiments, a criteria for determining the recommendation mayinclude level of anticipated human oversight. This may include, amongothers, understanding the level and types of decisions delegated tohuman workers (such as a decision to purchase a security, taking amarket decision, taking a license on Intellectual property, financiallimits on actions and ordering (e.g. is the RPA able to order or committo transactions below a certain amount, above which a human isinvolved), the level and type of anticipated human supervision ofrobotic process automation operations, anticipated extent of humansupervision and/or governance of model training and training data setselection. A further consideration may be the level and type ofanticipated human involvement in the curation of model versions (such asidentifying historical break points where input data should bediscarded); and others.

In embodiments, criteria for determining the recommendation may includesecurity considerations such as adversarial training and complexenvironments such as network attacks, viruses, and the like. Additionalsecurity considerations may include the security and management ofhistoric training datasets, including audit trails. Securityconsiderations may include the model traceability and accuracy—how willthe model or controlling parameters be updated, who will have authorityto update the model, how will the updates be documented, how willresults be correlated with model updates, and the like. How will versioncontrol be implemented and documented. Another security considerationwill be documentation of the results of the AI for audit trailsincluding financial results and performance results.

In embodiments, criteria for determining the recommendation may includethe availability of different AI types, models, algorithms, or systems(including heuristic/model-based AI, neural networks, and others).Availability may be limited by the computational environment that theuser intends to use such as a given cloud platform, an on-premises ITsystem, or in a network (edge or other network), and the like andwhether a given type, model, or algorithm will run in the client'senvironment. In embodiments, computational factors and configurationsmay be criteria. For example, the available processor types for runningthe AI solution in the client's environment may be a factor including:chipsets, modules, device, cloud components, number, and architecture ofprocessor types (e.g. multi-core processor availability, GPUavailability, CPU availability, FPGA availability, custom ASICavailability, and the like), and the like. Additionally, computationalfactors, which may be expressed as minimum capability criteria, mayinclude available processing capacity, both for solution training (forexample utilizing a cloud computing resource) and solution operationdeployment environment/capacity (e.g. IoT, in-vehicle, edge, meshnetwork, on-premises IT solution, stand along, or other deploymentenvironments). Additional criteria may include software and interfacecriteria such as software environment such as operating systems (Linux,Mac, PC, and the like), languages and protocols used for APIs for accessto input data sources for solution training as well as access to runtimedata and data integration and output.

In embodiments, criteria may include various network factors such asavailable network type, available network bandwidth (input and output)for both AI solution and AI operation, network uptime, networkredundance, variability of delivery times (sequencing of data may vary),as well as any of the other networks and network criteria describedherein.

In embodiments, criteria may include performance or quality of servicefactors, either in absolute terms or relative to other AI and/or non-AIsolutions (e.g. conventional models or rule-based solutions. Criteriamay include speed/latency, time to train/configure and an AI solution,time for the AI solution to provide result in an operations situation,accuracy, reliability (e.g. ability to resolve to a result),consistency, absence of bias, outcome-based measures of quality such asreturn on investment (ROI), yield (e.g. output from an AI-governedoperation), profitability, revenue and other economic measures,performance on safety measures, performance on security measures, energyconsumption (e.g. overall consumption, timing-based consumption (e.g.ability to shift processing from peak to off-peak hours), ability toaccess renewable or low-carbon energy for model training and/oroperation, management of cost of new model training initiatives (powercosts, latency and validation of new models), and the like.

In embodiments, criteria may include the ability of the client to accessa given type or model due to license requirements and limitations,client policies (described elsewhere herein), regulations (including inthe client's jurisdiction, the jurisdiction of the data source (e.g.European data privacy laws and Safe Harbor), a jurisdiction governing aparticular model, algorithm, or the like (e.g. export controls ontechnology), permissions (e.g. training data or operational data), andthe like. Additionally, the recommendation may be influenced by the typeof problem to be solved and whether there are specialized algorithms ormethods that are optimized for the type of problem (e.g. quantumannealing based traveling salesperson solver or even classic heuristicmethods that provide for reasonable baseline results).

In embodiments, criteria may include conformance or adherence togovernance principles and policies. There may be policies regarding whatinput data sources may be used to train the AI solution. There may bepolicies regarding what input sources may be used during operation. Forexample, input data sources may be reviewed for potential bias,appropriate representation (either demographically or of the problemspace), scope, and the like. There may be criteria regardingaccreditation or approval of the solution by a regulatory body,certification organization, internal IT review, and the like. There maybe policies and procedures that must be in place or implemented withrespect to security (e.g. physical security of the system,cybersecurity, and the like), safety requirements (e.g. the safety ofthe user, the safety of output product, and the like), and the like.

In embodiments, the criteria for recommending an AI solution may includecriteria regarding data availability such as the availability of datasources of adequate size, granularity, quality, reliability, location,time zones, accuracy, or the like for effective model training.Additional criteria regarding data availability may include the cost ofdata for: inputs for the model training, input for model operation.Additional criteria may include the availability of data for operationof the AI solution, and the like. Criteria for AI selection may furtherinclude upstream data processing requirements, master data managementconsiderations such as dimensional cleanup and data validation, and thelike.

In embodiments, criteria for solution selection may includeapplicability of the model or solution to the given task or workflow ofthe “problem” Criteria may include benchmark performance of a givenmodel relative to other models performing a known task type (e.g. aconvolutional neural network for 2D object classification, a gatedrecurrent neural network for tasks that tend to produce explodingerrors, or the like). In embodiments, selection of a solution may bebased on the solution having a configuration that is similar oranalogous to how a biological brain solves a similar task (e.g. where asequence of neural network models are arranged to mimic a sequence orflow which may include serial elements, parallel elements, feedbackloops, conditional logic junctions, graph-driven elements and other flowcharacteristics), such as a flow of modular or quasi-modular processes,such as ones involved in the brain of a human or other species, such asfor in visual or auditory processing, language recognition, speech,motion tracking, image recognition, facial recognition, motioncoordination, tactile recognition, spatial orientation, and the like.Criteria may include application of class AI heuristic methods tofunction as guard rails or operations in less impactful areas.

In embodiments, criteria may include model deployment considerationssuch as requirements for model updates (e.g. frequency and requirementfor retirement of models), management of historic models and maintaininghistorical decision engine, potential for distributed decision makingcapabilities, model curation rules (e.g. how long a model or input dataare considered valid for training), and the like.

Search results or recommendations may, in embodiments, be based at leastin part on collaborative filtering, such as by asking developers toindicate or select elements of favorable models, as well as byclustering, such as by using similarity matrices, k-means clustering, orother clustering techniques that associate similar developers, similardomain-specific problems, and/or similar artificial intelligencesolutions. The artificial intelligence store 157 may include e-commercefeatures, such as ratings, reviews, links to relevant content, andmechanisms for provisioning, licensing, delivery and payment (includingallocation of payments to affiliates and or contributors), includingones that operate using smart contract and/or blockchain features toautomate purchasing, licensing, payment tracking, settlement oftransactions, or other features.

In embodiments, once a solution has been selected or recommended, thesolution must be configured for the specific client and problem to besolved. Without limitation, configuration may include any of the factorsmentioned in connection with the selection of a solution model above.Configuration of a set of neural network types (e.g., modules) in a flow(with options for serial elements, parallel elements, feedback loops,conditional logic junctions, graph-driven flows and the like) thatrecognizes the relative strengths and weaknesses of each type of AIsolution (based on any or the selection factors noted above) for thespecific task involved in the flow is critical. In an illustrative andnon-limiting example of a flow, a) identify something by visualclassification (such as with a CNN), b) predict its future state (suchas with a gated RNN), c) optimize the future state (using a feed forwardneural network). Configuration options include selection of neuralnetwork type(s) (including hybrids of different neural networks and/orother model types in various flows as noted above); selection of inputmodel type; setting of initial model weights; setting model size (e.g.,number of layers in a deep neural network); selection of computationaldeployment environment; selection of input data sources for training;selection of input data sources for operation; selection of feedbackfunction/outcome measures; selection of data integration language(s) forinputs and outputs; configuration of APIs for model training;configuration of APIs for model inputs; configuration of APIs foroutputs; configuration of access controls (role-based, user-based,policy-based and others); configuration of security parameters;configuration of network protocols; configuration of storage parameters(type, location, duration); configuration of economic factors (e.g.,pricing for access; cost-allocation; and others); and others. Additionalconfiguration options may include configuration of data flows (e.g.flows from multiple security exchanges into centralized decisionengines), configuration of high availability, fault toleranceenvironments (e.g. trading systems are required to fail down tooperation state that meets services levels requirements), price baseddata acquisition strategies (e.g. detailed financial data may requireadditional spending), combination with heuristic methods, coordinationof massively parallel decision making environments (e.g. distributedvision systems), and the like. Additional configurations may includemaking decision models if there is an area that requires furtherconsideration (e.g. pushing a decision to the edge to monitor for aspecific event).

In embodiments, another set of solutions, which may be deployed alone orin connection with other elements of the platform, including theartificial intelligence store 157, may include a set of functionalimaging capabilities 161, which may comprise monitoring systems 164 anddata collection systems 166 and, in some cases, physical processobservation systems 162 and/or software interaction observation systems160, such as for monitoring various transactional and marketplaceentities 198. Functional imaging systems 161 may, in embodiments,provide considerable insight into the types of artificial intelligencethat are likely to be most effective in solving particular types ofproblems most effectively. As noted elsewhere in this disclosure and inthe documents incorporated by reference herein, computational andnetworking systems, as they grow in scale, complexity andinterconnections, manifest problems of information overload, noise,network congestion, energy waste, and many others. As the Internet ofThings grows to hundreds of billions of devices, and virtually countlesspotential interconnections, optimization becomes exceedingly difficult.One source for insight is the human brain, which faces similarchallenges and has evolved, over millennia, reasonable solutions to awide range of very difficult optimization problems. The human brainoperates with a massive neural network organized into interconnectedmodular systems, each of which has a degree of adaptation to solveparticular problems, from regulation of biological systems andmaintenance of homeostasis to detection of a wide range of static anddynamic patterns, to recognition of threats and opportunities, amongmany others.

Setting up a robotic process automation (RPA) system includes selectionof the best AI solution and configuration. There may be goals to trainthe RPA system, typically on human interactions with software and orhardware (e.g., tools) and to use the system in operation, both of whichbe enhanced by understanding what is going on in the human brain as itsolves a problem. In a single neural network solution (using one networkto solve a problem in a single step, like single-step translation), theprocess would likely involve setting initial weights for inputs,selection of input data sources, selection of the type of network (e.g.,convolutional or not, gated or not, deep or not, among others), thenumber of layers, and what inputs are provided to it (and outputs ifthere are complex outputs). The idea would be to pick inputs and weightsthat are the ones the human brain tends to use to solve the sameproblem. For hybrids of multiple AI modules/systems and/or AI combinedwith more conventional software systems (like control systems, analyticmodels, rule-based systems, conditional logic systems, and others), thevalue would likely be the above, plus configuring with awareness of timesequences of processing, such as reflecting patterns of brain activityas visual, auditory, tactile and other sensory information is processedto recognize situation, context, motion, objects, etc. and then otherregions (that behave differently) to do things like solve a logicpuzzle, calculate, follow an algorithm, proliferate possibilities, andmany others. For these, a series of “lego blocks”, each consisting of adifferent neural network or other AI type, can be sequenced, set inparallel, linked by conditional logic, etc. to achieve a solution thatautomate the process.

In embodiments, identification of a type of reasoning and/or a type ofprocessing may be informed by undertaking brain imaging, such asfunctional MRI or other magnetic imaging, electroencephalogram (EEG), orother imaging, such as by identifying broad brain activity (e.g., wavebands of activity, such as delta, theta, alpha and gamma waves), byidentifying a set of brain regions that are activated and/or inactiveduring the set interactions of the user that are being used for trainingof the intelligent agent (such as neocortex regions, such as Fp1(involved in judgment and decision making), F7 (involved in imaginationand mimicry), F3 (involved in analytic deduction), T3 (involved inspeech), C3 (involved in storage of facts), T5 (involved in mediationand empathy), P3 (involved in tactical navigation), O1 (involved invisual engineering), Fp2 (involved in process management), F8 (involvedin belief systems), F4 (involved in expert classification), T4 (involvedin listening and intuition), C4 (involved in artistic creativity), T6(involved in prediction), P4 (involved in strategic gaming), O2(involved in abstraction), and/or combinations of the foregoing) or byother neuroscientific, psychological, or similar techniques that provideinsight into how humans upon which the intelligent agent is trained aresolving particular types of problems that are involved in workflows forwhich intelligent agents are deployed. In embodiments, an intelligentagent may be configured with a neural network type, or combination oftypes, that is selected to replicate or simulate a processing activitythat is similar to the activity of the brain regions of a human expertthat is performing a set of activities for which the intelligent agentis to be trained. As one example among many possible, a trader may beshown to use visual processing region O1 and strategic gaming region P4of the neocortex when making successful trades, and a neural network maybe configured with a convolutional neural network to provide effectivereplication of visual pattern recognition and a gated recurrent neuralnetwork to replicate strategic gaming. In embodiments, a library ofneural network resources representing combinations of neural networktypes that mimic or simulate neocortex activities may be configured toallow selection and implementation of modules that replicate thecombinations used by human experts to undertake various activities thatare subjects of development of intelligent agents, such as involvingrobotic process automation. In embodiments, various neural network typesfrom the library may be configured in series and/or in parallelconfigurations to represent processing flows, which may be arranged tomimic or replicate flows of processing in the brain, such as based onspatiotemporal imaging of the brain when involved in the activity thatis the subject of automation. In embodiments, an intelligent softwareagent for agent development may be trained, such as using any of thetraining techniques described herein, to select a set of neural networkresource types, to arrange the neural network resource types accordingto a processing flow, to configure input data sources for the set ofneural network resources, and/or to automatically deploy the set ofneural network types on available computational resources to initiatetraining of the configured set of neural network resources to perform adesired intelligent agent/automation workflows. In embodiments, theintelligent software agent used for agent development operates on aninput data set of spatiotemporal imaging data of a human brain, such asan expert who is performing the workflows that is the subject ofdevelopment of a further, and uses the spatiotemporal imaging data toautomatically select and configure the selection and arrangement of theset of neural network types to initiate learning. Thus, a system fordeveloping an intelligent agent may be configured for (optionallyautomatic) selection of neural network types and/or arrangements basedon spatiotemporal neocortical activity patterns of human users involvedin workflows for which the agent is trained. Once developed, theresulting intelligent agent/process automation system may be trained asdescribed throughout this disclosure.

In embodiments, a system for developing an intelligent agent (includingthe aforementioned agent for development of intelligent agents) may useinformation from brain imaging of human users to infer (optionallyautomatically) what data sources should be selected as inputs for anintelligent agent. For example, for processes where neocortex region O1is highly active (involving visual processing), visual inputs (such asavailable information from cameras, or visual representations ofinformation like price patterns, among many others) may be selected asfavorable data sources. Similarly, for processes involving region C3(involving storage and retrieval of facts), data sources providingreliable factual information (such as blockchain-based distributedledgers) may be selected. Thus, a system for developing an intelligentagent may be configured for (optionally automatic) selection of inputdata types and sources based on spatiotemporal neocortical activitypatterns of human users involved in workflows for which the agent istrained.

Functional imaging 161, such as functional magnetic resonance imaging(fMRI), electroencephalogram (EEG), computed tomography (CT) and otherbrain imaging systems have improved to the point that patterns of brainactivity can be recognized in real time and temporally associated withother information, such behaviors, stimulus information, environmentalcondition data, gestures, eye movements, and other information, suchthat via functional imaging 161, either alone or in combination withother information collected by monitoring systems 164, the platform maydetermine and classify what brain modules, operations, systems, and/orfunctions are employed during the undertaking of a set of tasks oractivities, such as ones involving software interaction observationsystems 160, physical process observations 162, or a combinationthereof. This classification may assist in selection and/orconfiguration of a set of artificial intelligence solutions, such asfrom an artificial intelligence store 157, that includes a similar setof capabilities and/or functions to the set of modules and functions ofthe human brain when undertaking an activity, such as for the initialconfiguration of a robotic process automation (RPA) system 154 thatautomates a task performed by an expert human.

In embodiments, a system may receive and/or monitor a set of inputsrelating to a user, including image/video feeds, audio feeds, motionsensors, heartbeat monitor, other relevant biosensors, and the like. Inembodiments, the system may also receive input relating to actions takenby the monitored user, such as input to a computing device or actionstaken with respect to a physical environment in which the user isworking. In embodiments, all the collected data is time stamped, sothat, for example, a video feed may capture a series of images of a userwhile the user is performing a task and may concurrently capture the eyemovements of the user (e.g. eye gaze tracking) to determine what theuser is focusing on (e.g., what is the user looking at on a screen).During this time, the system may also track the user's heart rate orother biological sensor measurements to determine whether the user isengaged in a task that requires intense concentration or less focusedconcentration. The system may also track the actions taken and mayfurther determine the amount of time taken between actions. An RPAsolution can then distribute processing, such as to a heavier, morecomputationally intensive activity to an AI solution on a cloud platform(like a deep neural network with many layers) and placing lesscomputationally intensive tasks, such as ones where a human makes veryquick decisions on minimal input data, on an edge or IoT device platformusing a much more compact model, such as a TinyML™ model.

In embodiments, the system may determine the relative amount of timetaken between actions, such that long periods of inaction may indicatethat the user is involved in work that requires lots of thought, whileshort periods of inaction may indicate that the user is engaged in workthat requires less thought and more action. The system may also monitoran audio feed and/or state of the computing device that a user isworking on when the period of inaction occurs, which may be indicativeof a user being distracted rather than focusing. Assuming that the useris actively working and not exhibiting distraction, then the system cangenerate a feature vector relating to the work being performed by theuser that indicates the time-stamped data entries, which can be then fedinto a machine-learned model. In embodiments, the machine-learned modelmay determine a brain region (or multiple brain regions) from the set ofbrain regions that were likely engaged during the work period. Inembodiments, the machine-learned model may be trained using a trainingdata set that includes labeled training vectors, where the label of eachtraining vector indicates the brain region (or regions) that were beingengaged by a subject when the training vector was generated. Forexample, each training vector may be labeled with one or more of: Fp1(involved in judgment and decision making), F7 (involved in imaginationand mimicry), F3 (involved in analytic deduction), T3 (involved inspeech), C3 (involved in storage of facts), T5 (involved in mediationand empathy), P3 (involved in tactical navigation), O1 (involved invisual engineering), Fp2 (involved in process management), F8 (involvedin belief systems), F4 (involved in expert classification), T4 (involvedin listening and intuition), C4 (involved in artistic creativity), T6(involved in prediction), P4 (involved in strategic gaming), O2(involved in abstraction)). In some embodiments, the training vector mayindicate additional data, such as the type of task being performed,whether the subject was successful in completing the task, or othersuitable information.

In embodiments, these machine-learned models may be trained on differenttypes of work tasks, such as negotiating, drafting, data entry,responding to emails, analyzing data, reviewing documents, or the like.Furthermore, in some embodiments, such machine-learned models may betrained by one party but leveraged by other parties. In theseembodiments, the machine-learned models (and/or the training datavectors) may be bought and sold via a marketplace. Such machine-learnedmodels may be used in a broader RPA system, such that the output of themodels may be used as a specific signal in an RPA learning process.

In general, using data from organizations for predicting positioning oforganization in market and adjusting processes within organizationaccordingly. In example embodiments, robotic imaging may be used tocapture data of users (e.g., employees or workers) within theorganization as they complete various tasks and processes while alsocorrelating this information with completion of these tasks/processes.Obtaining various analytics regarding success of completion of tasks(e.g., efficiency). Then, using data obtained from tracking/monitoringusers to determine what factors indicate some users as being moresuccessful than other users in completion of tasks (e.g., based onphysical movements of users in doing tasks correctly, brain regionsactivated, physical strength of users, etc.). This may be based onscanning/monitoring of users as they complete tasks. In some exampleembodiments, using system to segregate data relating to users withsuccessful task completions versus data relating to users with lesssuccessful completions. The system may analyze biological data ofworkers to determine what makes one worker more successful than otherworkers. In some example embodiments, this analysis may also be combinedwith data from machines to determine whether workers are using machinesaccurately/efficiently. This biological data from workers may also beused to determine whether more workers may be needed to improveefficiency. Using historical data and results from process competitionsto look at what improvements should be made whether by training,selecting workers who are better are some tasks vs. others, etc. Theresulting analytics on outcomes, and contributions to outcomes, may beused, for example, as a feedback function for weighting the value ofparticular capabilities for design of an AI solution that is intended toperform the same or similar tasks. In some example embodiments, variousdata and analysis as described above may be used with respect todetermining whether improvements made based on the analysis alsoimproves the market positioning of the organization.

An operator skilled in a task may develop strong memory connections tomuscle functions—muscle memory—which translates into easily accomplishedactions that, without this connection, would be difficult or at leastrequire repeated attempts, slower operation, and the like. A system thatcan distinguish between actions accomplished using muscle memory andothers may better identify which actions are worthfollowing/repeating/learning.

Understanding the mechanisms of muscle memory—e.g., understanding thepathways from cognition (visual, auditory, etc.) inputs to developmuscle memory may be a basis for understanding how to automate humanactions. This may involve repetition type actions, association of onetype of action with another type of action based on similarities, suchas body positioning, expected result (dropping the hammer in theholster, etc.).

Additional value might be in understanding how two individuals candevelop a form of muscle memory that allows them to “get into a rhythm”,such as when exchanging physical items. What cues are they exchanging,visually recognizable actions (placement of hand/orientation) and howare those interpreted.

In embodiments, an imaging system may analyze brain images of multiplemembers of a team for a set of tasks or workflows that involve differenttypes of expertise. Team performance can be tracked, and AI solutionsmay be configured to replicate the types of neural processing that areundertaken by different team members, such as motion tracking andcoordination by one team member and executive decision making byanother.

In embodiments, an imaging system may analyze brain images of multiplemembers of a mock trial or negotiation practice sessions for a set ofverbal exchanges regarding an argument, point-count-point, and the likefor negotiations, and the like. In addition to brain images, audiocapture and bio-indicators of response to exchanges could also beharvested to increase the range of multi-dimensional data useful forlearning how to automate human actions associated with successfulnegotiation and the like.

Given the level of abstraction humans use to trigger actions, e.g.recognizing an alarm tone or recognizing an action from a fellow worker,we can get less abstract in machine-machine communication, e.g. theinput that triggered the alarm tone can trigger a direct machine-machinecommunication or, if the fellow worker is now a machine, they canindicate their positioning in their routine indicating they are ready tohand-off their work. This is similar to how less intelligent robots havebeen automated, even with simple macros where the “intelligence” iswrung out of the process to make it more robust, and there arestrategies and methods for this that could be applied to thesebiologic-type inputs which are a level of abstraction beyond what isneeded. This down-shift in complexity can, itself, be trained into thesystem as they recognize what myriad of “soft” triggers (e.g. imagerecognition) can be turned into “hard” triggers.)

Using systems like Fp1 (involved in judgment and decision making), P3(involved in tactical navigation), O1 (involved in visual engineering),Fp2 (involved in process management), F8 (involved in belief systems),and T4 (involved in listening and intuition), the training vectors mayindicate, in some embodiments, a system of mixed audio and visualconcepts. The system may use an expert system to monitor a set of inputsand reconfigure those inputs to monitor an asset including image feedsat various electromagnetic frequencies (such as visual light, thermal,UV, and the like), and audio feeds from those frequencies to determineuse, sounds of use, and possible sounds of concerns. When examplesinclude fixed assets (those that cannot move), ambient measurement ofthe environment may be measured along with signatures of use or non-useof the product such as lack of motion, thermal imprints, or lackthereof. The changing environment in the room, the contact with asset byuser or other fixtures, can cause reconfiguration of the sensors lookingto appreciate the space. When fixed in a room, such systems maydetermine that ambient conditions could be detrimental to the asset suchas strong outside lighting (too rich of UV content) relative to moreappropriate lighting. Also included is sensing the motion of use. Inmore moveable assets, detection and parsing of benign motion rather thanmotion that may have a higher propensity to age or damage an asset canbe recorded and characterized as an aggregated feed.

Risk Management—Combination of F3 (analytic deduction) and Fp1(judgement and decision making)—Analytics and decision making in thehuman brain are informed by experience and knowledge, which may bepartial, limited, negative, positive, factual, emotional, etc. AI canpossibly recognize a situation (sensors, image recognition, proximity,text and conversation analysis, etc.), and apply better risk managementin decision making using stored fact-based outcomes for similarsituations. This could be applied to enable consumers to make betterpurchasing and financial decisions. In other applications, it could beapplied to emergency response, policing actions, etc.

In embodiments, an AI solution may be configured as a companion riskmanager for a main operational AI solution, such as sharing commoninputs and resources, but focused on identifying risks, externalities,and other factors that are not required for the core process automation,but may improve governance, safety, emergency response, and otheraspects.

In embodiments, an AI solution may be configured as a companion riskmanager for a main operational AI solution, such as sharing commoninputs and resources, but focused on identifying risks, externalities,and other factors that are not required for the core process automation,but may improve governance, safety, emergency response, and otheraspects.

Thus, the platform may include a system that takes input from afunctional imaging system to configure, optionally automatically basedon matching of attributes between one or more biological systems, suchas brain systems, and one or more artificial intelligence systems, a setof artificial intelligence capabilities for a robotic process automationsystem. Selection and configuration may further comprise selection ofinputs to robotic process automation and/or artificial intelligence thatare configured at least in part based on functional imaging of the brainwhile workers undertake tasks, such as selection of visual inputs (suchas images from cameras) where vision systems of the brain are highlyactivated, selection of acoustic inputs where auditory systems of thebrain are highly activated, selection of chemical inputs (such aschemical sensors) where olfactory systems of the brain are highlyactivated, or the like. Thus, a biologically aware robotic processautomation system may be improved by having initial configuration, oriterative improvement, be guided, either automatically or underdeveloper control, by imaging-derived information collected as workersperform expert tasks that may benefit from automation.

Functional imaging may provide insight into which tasks involve serialprocessing versus parallel processing, providing insight into the typeof AI solution that may be best suited to a similar tasks (e.g. is itbest to receive language and visual data/inputs at once (in parallel) orsequentially). Is there an order in which a user takes in data thatmight suggest an optimal ordering for performance? Analysis offunctional images may enable identification of which computations tasksare most quickly processed through visual inputs versus textual(language processing) and may enable improved matching of task to bestinput/stimulus.

Functional imaging may enable determining efficiencies resulting fromthe pairing or multiple combinations of stimuli (e.g., is a task/commandmost efficiently communicated by providing multiple, diverse inputs atonce, and/or is it best to omit certain stimuli from inputs/commands.

Functional imaging may enable ranking tasks or events to perform/solvebased on the probabilistic improvement in the performance of asubsequent task (where task could be a computation or an actual actionperformed by a device based on a data/stimulus input).

Functional imaging may enable measuring negative impacts onperformance/computation based on “noise,” where noise may be unneededdata, irrelevant data, or overwhelming data sizes—similar to determining“negative stimuli” (in the human context this could be ambient noise indistinguishing a human voice within a cascade of auditory inputs, orambient lighting in image recognition, or movement in counting objectsin a region and so forth.

As one example among many possible, a marketplace host may be shown touse prediction region T6 and judgment and decision making region Fp1when configuring a new marketplace, such as to predict favorablemarketplace configuration parameters (such as to optimize marketplaceefficiency profitability, and/or fairness) and to generate decisionsrelated to marketplace parameters, and a neural network may beconfigured with a neural network to provide effective replication ofprediction and a neural network to replicate decision making. Themarketplace configuration parameters may include, but are not limitedto, assets, asset types, description of assets, method for verificationof ownership, method for delivery of traded goods, estimated size ofmarketplace, methods for advertising the marketplace, methods forcontrolling the marketplace, regulatory constraints, data sources,insider trading detection techniques, liquidity requirements, accessrequirements (such as whether to implement dealer-to-dealer trading,dealer-to-customer trading, or customer-to-customer trading), anonymity(such as determining whether counterparty identities are disclosed),continuity of order handling (e.g., continuous or periodic orderhandling), interaction (e.g., bilateral or multilateral), pricediscovery, pricing drivers (e.g., order-driven pricing or quote-drivenpricing), price formation (e.g., centralized price formation orfragmented price formation), custodial requirements, types of ordersallowed (such as limit orders, stop orders, market orders, andoff-market orders), supported market types (such as dealer markets,auction markets, absolute auction markets, minimum bid auction markets,reverse auction markets, sealed bid auction markets, Dutch auctionmarkets, multi-step auction markets (e.g., two-step, three-step, n-step,etc.), forward markets, futures markets, secondary markets, derivativesmarkets, contingent markets, markets for aggregates (e.g., mutualfunds), and the like), trading rules (e.g., tick size, trading halts,open/close hours, escrow requirements, liquidity requirements,geographic rules, jurisdictional rules, rules on publicity, insidertrading prohibitions, conflict of interest rules, timing rules (e.g.,involving spot-market trading, futures trading and the like) and manyothers), asset listing requirements (e.g., financial reportingrequirements, auditing requirements, minimum capital requirements),deposit minimums, trading minimums, verification rules, commissionrules, fee rules, marketplace lifetime rules (e.g., short-termmarketplace with timing constraints vs. long-term marketplace), andtransparency (e.g., the amount and extent of information disseminated).

An RPA system may use AI systems related to biological brain functionsF3 (involved in analytic deduction) and O1 (involved in visualengineering) in conjunction with one another to perform tasks related tovisual calculus. The tasks related to visual calculus may include, forexample, processing image sensor data via the O1 visual engineeringsystem to determine what the RPA system “sees,” and how to interpret,classify, identify, etc. what is “seen.” Then, the F3 analytic deductionsystem may perform 1) deductions to determine what has led to thecurrent state of what is “seen,” and 2) prediction to determine a futurestate of what is “seen” based on the current state of visual data. TheRPA system may use the T6 prediction function to assist in performingsuch predictions. The deductions may be useful in determining a cause ofan issue, inefficiency, or problem in a system being analyzed. Thepredictions may be useful in determining solutions to problems and/orpotential efficiency improvements. The AI system using F3, O1, and/or T6may then also be used to choose a machine learned model suitable forperforming the problem solving and/or efficiency improvement. Forexample, in a manufacturing environment, the RPA system and AI systemmay intake data from a plurality of visual IoT sensors, the visual databeing from one or more sites on the manufacturing floor. The O1 visualengineering system may determine and/or classify what the visual data isseeing, such as one or more machines, products, assembly lines, etc. TheF3 analytic deduction system may determine whether one or more of themachines, products, assembly lines, etc. are indicative of issues orinefficiencies. The T6 system may then make predictions and forward thepredictions to a suitable machine learned model for determiningsolutions to problems and/or improvements to efficiencies.

IoT and Onboard Sensor Platform for Monitoring Collateral for a Loan

In embodiments, provided herein is a platform, consisting of variousservices, components, modules, programs, systems, devices, algorithms,and other elements, for monitoring collateral for a loan. Inembodiments, the platform or system includes (a) a set of Internet ofThings services for monitoring an environment for the collateral; a setof sensors positioned on at least one of the collateral, a container forthe collateral, and a package for the collateral, the set of sensorsconfigured to associate sensor information sensed by the set of sensorswith a unique identifier for the collateral; and a set of blockchainservices for taking information from the set of Internet of Thingsservices and the set of sensors and storing the information in ablockchain, wherein access to the blockchain is provided via a secureaccess control interface for a secured lender for a loan to which thecollateral is subject.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the collateral items are selected from among a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the set of Internet of Things services monitors anenvironment selected from among a real property environment, acommercial facility, a warehousing facility, a transportationenvironment, a manufacturing environment, a storage environment, a home,and a vehicle.

In embodiments the set of sensors is selected from the group consistingof image, temperature, pressure, humidity, velocity, acceleration,rotational, torque, weight, chemical, magnetic field, electrical field,and position sensors.

In embodiments the platform or system may further include a set ofservices for reporting on events relevant to at least one of the value,the condition and the ownership of the collateral.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of the collateral and undertakes an actionrelated to a loan to which the collateral is subject.

In embodiments the loan-related action is selected from among offering aloan, accepting a loan, underwriting a loan, setting an interest ratefor a loan, deferring a payment requirement, modifying an interest ratefor a loan, validating title for collateral, recording a change intitle, assessing the value of collateral, initiating inspection ofcollateral, calling a loan, closing a loan, setting terms and conditionsfor a loan, providing notices required to be provided to a borrower,foreclosing on property subject to a loan, and modifying terms andconditions for a loan.

In embodiments the platform or system may further include a market valuedata collection service that monitors and reports on marketplaceinformation relevant to the value of the collateral. In embodiments thecollateral items are selected from among a vehicle, a ship, a plane, abuilding, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

In embodiments the market value data collection service monitors pricingor financial data for items that are similar to the collateral in atleast one public marketplace.

In embodiments a set of similar items for valuing an item of collateralis constructed using a similarity clustering algorithm based on theattributes of the collateral. In embodiments the attributes are selectedfrom among a category of the collateral, an age of the collateral, acondition of the collateral, a history of the collateral, a storagecondition of the collateral and a geolocation of the collateral.

In embodiments the platform or system may further include a set of smartcontract services for managing a smart contract for the loan. Inembodiments the smart contract services set terms and conditions for theloan. In embodiments the set of terms and conditions for the loan thatare specified and managed by the set of smart contract services isselected from among a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a specification ofcollateral, a specification of substitutability of collateral, a party,a guarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default.

Allocate Collateral for a Loan Using Distributed Ledger and SmartContract

In embodiments, provided herein is a system for handling a loan having aset of computational services. In embodiments, the platform or systemincludes (a) a set of blockchain services for supporting a distributedledger; (b) a set of data collection and monitoring services formonitoring a set of items that provide collateral for a loan; (c) a setof valuation services that use a valuation model to set a value forcollateral based on information from the data collection and monitoringservices; and (d) a set of smart contract services for establishing asmart lending contract, wherein the smart contract services processoutput from the set of valuation services and assigns items ofcollateral sufficient to provide security for the loan to the loan on adistributed ledger that records events relevant to the loan.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

In embodiments the collateral items are selected from among a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the valuation services include artificial intelligenceservices that iteratively improve the valuation model based on outcomedata relating to transactions in collateral.

In embodiments the valuation services further include a set of marketvalue data collection services that monitor and report on marketplaceinformation relevant to the value of collateral.

In embodiments the set of market value data collection services monitorspricing or financial data for items that are similar to the collateralin at least one public marketplace.

In embodiments a set of similar items for valuing an item of collateralis constructed using a similarity clustering algorithm based on theattributes of the collateral.

In embodiments the attributes are selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral and ageolocation of the collateral.

Smart Contract that Sets Primary and Secondary Priority for Lenders onSame Collateral

In embodiments, provided herein is a system for handling a loan having aset of computational services. In embodiments, the platform or systemincludes (a) a set of blockchain services for supporting a distributedledger; (b) a set of data collection and monitoring services formonitoring a set of items that provide collateral for a loan; and (c) aset of smart contract services for establishing a smart lendingcontract, wherein the smart contract services assign collateral to aloan on a distributed ledger that records events relevant to the loanand record priority among a set of lending entities with respect to thecollateral.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

In embodiments the set of the collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the platform or system may further include a set ofvaluation services that use a valuation model to set a value forcollateral based on information from a set of data collection andmonitoring services that monitor items of collateral.

In embodiments the valuation services include artificial intelligenceservices that iteratively improve the valuation model based on outcomedata relating to transactions in collateral.

In embodiments the valuation services further include a set of marketvalue data collection services that monitor and report on marketplaceinformation relevant to the value of collateral.

In embodiments the set of market value data collection services monitorspricing or financial data for items that are similar to the collateralin at least one public marketplace.

In embodiments a set of similar items for valuing an item of collateralis constructed using a similarity clustering algorithm based on theattributes of the collateral.

In embodiments the attributes are selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral and ageolocation of the collateral.

In embodiments output from the set of valuation services is used by thesmart contract services to apportion value for an item of collateralamong a set of lenders.

In embodiments the apportionment of value is based on priorityinformation for the lenders that is recorded in the distributed ledger.

Referring to FIG. 3, in embodiments, devices 252 may be connecteddevices that connect (such as through any of the wide range ofinterfaces 187) to a set of Internet of Things (IoT) data collectionservices 208, which may be part of or integrated with the datacollection systems 166 and monitoring systems 164 of the lendingenablement platform 100. The interfaces 187 may include networkinterfaces, APIs, SDKs, ports, brokers, connectors, gateways, cellularnetwork facilities, data integration interfaces, data migration systems,cloud computing interfaces (including ones that include computationalcapabilities, such as AWS IoT Greengrass™, Amazon™ Lambda™ and similarsystems), and others. For example, the IoT data collection services 208may be configured to take data from a set of edge data collectiondevices in the Internet of Things, such as low-power sensor devices(e.g., for sensing movement of entities, for sensing, temperatures,pressures or other attributes about entities 198 or their environments,or the like), cameras that capture still or video images of entities198, more fully enabled edge devices (such as Raspberry Pi™ or othercomputing devices, Unix™ devices, and devices running embedded systems,such as including microcontrollers, FPGAs, ASICs and the like), and manyothers. The IoT data collection services 208 may, in embodiments,collect data about collateral 102 or assets 218, such as, for example,regarding the location, condition (health, physical, or otherwise),quality, security, possession, or the like. For example, an item ofpersonal property, such as a gemstone, vehicle, item of artwork, or thelike, may be monitored by a motion sensor and/or a camera having a knownlocation (or having a location confirmed by GPS or other locationsystem), to ensure that it remains in a safe, designated location. Thecamera can provide evidence that the item remains in undamaged conditionand in the possession of a party 210, such as to indicate that itremains appropriate and adequate collateral 102 for a loan. Inembodiments this may include items of collateral for microloans, such asclothing, collectibles, and other items.

In embodiments the lending enablement platform 100 has a set ofdata-integrated microservices including data collection services 166,monitoring services 164, blockchain services for storing data as ablockchain 136, and smart contract services 134 for handling lendingentities and transactions. The smart contract services 134 may take datafrom the data collection systems 166 and monitoring systems 164 (such asfrom TOT devices) and automatically execute a set of rules or conditionsthat embody the smart contract based on the collected data. For example,upon recognition that collateral 102 for a loan has been damaged (suchas evidenced by a camera or sensor), the smart contract services 134 mayautomatically initiate a demand for payment of a loan, automaticallyinitiate a foreclosure process, automatically initiate an action toclaim substitute or backup collateral, automatically initiate aninspection process, automatically change a payment or interest rate termthat is based on the collateral (such as setting an interest rate at alevel for an unsecured loan, rather than a secured loan), or the like.Smart contract events may be recorded on a blockchain 136 by theblockchain services, such as in a distributed ledger. Automatedmonitoring of collateral 102 and assets 218 and handling of loans viasmart contract services 134 may facilitate lending to a much wider rangeof parties 210 and undertaking of loans based on a much wider range ofcollateral 102 and assets 218 than for conventional loans, as lendersmay have greater certainty as to the condition of collateral. Monitoringsystems 164 and data collection systems 166 may also monitor and collectdata from external marketplaces 188 or for marketplaces operated withthe lending enablement platform 100 to maintain awareness of the valueof collateral 102 and assets 218, such as to ensure that items remain ofadequate value and liquidity to assure repayment of a loan. For example,public e-commerce auction sites like eBay™ can be monitored to confirmthat personal property items are of a type and condition likely to bedisposed of easily by a lender in a liquid public market, so that thelender is sure to receive payment if the borrower defaults. This mayallow loans to be made and administered on a wide range of personalproperty that is normally difficult to use as collateral. In embodimentsan automated foreclosure process may be initiated by a smart contract,which may, upon occurrence of a condition of default that permitsforeclosure (such as uncured failure to make payments) include a processfor automatically initiating placement of an item of collateral on apublic auction site (such as eBay™ or an auction site appropriate for aparticular type of property), automatically securing collateral (such asby locking a connected device, such as a smart lock, smart container, orthe like that contains or secures collateral), automatically configuringa set of instructions to a carrier, freight forwarder, or the like forshipping collateral, automatically configuring a set of instructions fora drone, a robot, or the like for transporting collateral, or the like.

In embodiments a system is provided for facilitating foreclosure oncollateral. The system may include a set of data collection andmonitoring services for monitoring at least one condition of a lendingagreement; and a set of smart contract services establishing terms andconditions of the lending agreement that include terms and conditionsfor foreclosure on at least one item that provides collateral securing arepayment obligation of the lending agreement, wherein upon detection ofa default based on data collected by the data collection and monitoringservices, the set of smart contract services automatically initiates aforeclosure process on the collateral. In embodiments, the set of smartcontract services initiates a signal to at least one of a smart lock anda smart container to lock the collateral. In embodiments, the set ofsmart contract services configures and initiates a listing of thecollateral on a public auction site. In embodiments, the set of smartcontract services configures and delivers a set of transportinstructions for the collateral. In embodiments, the set of smartcontract services configures a set of instructions for a drone totransport the collateral. In embodiments, the set of smart contractservices configures a set of instructions for a robot to transport thecollateral. In embodiments, the set of smart contract services initiatesa process for automatically substituting a set of substitute collateral.In embodiments, the set of smart contract services initiates a messageto a borrower initiating a negotiation regarding the foreclosure. Inembodiments, the negotiation is managed by a robotic process automationsystem that is trained on a training set of foreclosure negotiations. Inembodiments, the negotiation relates to modification of at least one ofthe interest rate, the payment terms, and the collateral for the lendingtransaction.

Referring to FIG. 4, in embodiments the lending enablement platform 100is provided having Internet of Things (IoT) data collection services 208(with various IoT and edge devices as described throughout thisdisclosure) for monitoring at least one of a set of assets 218 and a setof collateral 102 for a loan, a bond, or a debt transaction. The lendingenablement platform 100 may include a guarantee and/or securitymonitoring solution 230 for monitoring assets 218 and/or collateral 102based on the data collected by the IoT data collection services 208,such as where the guarantee and/or security monitoring solution 230 usesvarious adaptive intelligent systems 158, such as ones that may usemodel (which may be adjusted, reinforced, trained, or the like, such asusing artificial intelligence 156) that determines the condition orvalue of items based on images, sensor data, location data, or otherdata of the type collected by the IoT data collection services 208.Monitoring may include monitoring of location of collateral 102 orassets 218, behavior of parties 210, financial condition of parties 210,or the like. The guarantee and/or security monitoring solution 230 mayinclude a set of interfaces by which a user may configure parameters formonitoring, such as rules or thresholds regarding conditions, behaviors,attributes, financial values, locations, or the like, in order to obtainalerts regarding collateral 102 or assets 218. For example, a user mayset a rule that collateral must remain in a given jurisdiction, athreshold value of the collateral as a percentage of a loan balance, aminimum status condition (e.g., freedom from damage or defects), or thelike. Configured parameters may be used to provide alerts to personnelresponsible for monitoring loan compliance and/or used or embodied intoone or more smart contract contracts that may take input from theinterface of the guarantee and/or security monitoring solution 230 toconfigure conditions for foreclosure, conditions for changing interestrates, conditions for accelerating payments, or the like. The lendingenablement platform 100 may have a loan management solution 248 thatallows a loan manager to access information from the IoT data collectionservices 208 and/or the guarantee and/or security monitoring solution230, such that a user may manage various actions with respect to a loan(of the many types describe herein, such as setting interest rates,foreclosing, sending notices, and the like) based on the condition ofcollateral 102 or assets 218, based on events involving entities 198,based on behaviors, based on loan-related actions (such as payments) andother factors. The loan management solution 248 may include a set ofinterfaces, workflows, models (including adaptive intelligent systems158) that are configured for a particular type of loan (of the manytypes described herein) and that allow a user to configure parameters,set rules, set thresholds, design workflows, configure smart contractservices, configure blockchain services, and the like in order tofacilitate automated or assisted management of a loan, such as enablingautomated handing of loan actions by a smart contract in response tocollected data from the IoT data collection services 208 or enablinggeneration of a set of recommended actions for a human user based onthat data.

In embodiments a lending platform is provided having a smart contractand distributed ledger platform for managing at least one of ownershipof a set of collateral and a set of events related to a set ofcollateral. A set of smart contract services 134 may, for example,transfer ownership of the collateral 102 or other assets 218 uponrecognition of an event of failure to make payment or other default,occurrence of a foreclosure condition (such as failure to satisfy with acovenant or failure to comply with an obligation), or the like, wherethe ownership transfer and related events are recorded by the set ofblockchain services in a distributed ledger, such as one that provides asecure record of title to the assets 218 or collateral 102. As anexample, a covenant of a loan embodied in a smart contract may requirethat collateral 102 have a value that exceeds a minimum fraction (ormultiple) of the remaining balance of a loan. Based on data collectedabout the value of collateral (such as by monitoring one or moreexternal marketplaces 188 or marketplaces of the lending enablementplatform 100), a smart contract may calculate whether the covenant issatisfied and record the outcome on a blockchain. If the covenant is notsatisfied, such as if market factors indicate that the type ofcollateral has diminished, while the loan balance remains high, thesmart contract may initiate a foreclosure, including recording anownership transfer on a distributed ledger via the blockchain services.A smart contract may also process events related to an entity 198 suchas a party 210. For example, a covenant of a loan may require the partyto maintain a level of debt below a threshold or ratio, to maintain alevel of income, to maintain a level of profit, or the like. Themonitoring systems 164 or data collection systems 166 may provide dataused by the smart contract services 134 to determine covenant complianceand to enable automated action, including recording events likeforeclosure and ownership transfers on a distributed ledger. In anotherexample, a covenant may relate to a behavior of a party 210 or a legalstatus of a party 210, such as requiring the party to refrain fromtaking a particular action with respect to an item of property. Forexample, a covenant may require a party to comply with zoningregulations that prohibit certain usage of real property. IoT datacollection services 208 may be used to monitor the party 210, theproperty, or other items to confirm compliance with the covenant or totrigger alerts or automated actions in cases of non-compliance.

Smart Contract with Automatic Foreclosure Based on Collateral ValueFalling Below Covenant Requirement

In embodiments, provided herein is a system for handling a loan having aset of computational services. In embodiments, the platform or systemincludes (a) a set of data collection and monitoring services formonitoring a set of items that provide collateral for a loan; (b) a setof valuation services that uses a valuation model to set a value forcollateral based on information from the data collection and monitoringservices; and (c) a set of smart contract services for managing a smartlending contract, wherein the set of smart contract services processesoutput from the set of valuation services, compares the output to acovenant of the loan that is specified in a smart contract andautomatically initiates at least one of a notice of default and aforeclosure action when the value of the collateral is insufficient tosatisfy the covenant.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

In embodiments the set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the set of valuation services includes artificialintelligence services that iteratively improve the valuation model basedon outcome data relating to transactions in collateral.

In embodiments the set of valuation services further includes a set ofmarket value data collection services that monitor and report onmarketplace information relevant to the value of collateral.

In embodiments the set of market value data collection services monitorspricing or financial data for items that are similar to the collateralin at least one public marketplace.

In embodiments a set of similar items for valuing an item of collateralis constructed using a similarity clustering algorithm based on theattributes of the collateral.

In embodiments the attributes are selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral and ageolocation of the collateral.

Collateral for Smart Contract Aggregated with Other Similar Collateral

In embodiments, provided herein is a smart contract system for handlinga loan having a set of computational services. In embodiments, theplatform or system includes (a) a set of data collection and monitoringservices for identifying a set of items that provide collateral for aset of loans and collecting information with respect to the collateralitems; (b) a set of clustering services for grouping the collateralitems based on similarity of attributes of the collateral items; and (c)a set of smart contract services for managing a smart lending contract,wherein the set of smart contract services processes output from the setof clustering services and aggregates and links a subset of similaritems of collateral to provide collateral for a set of loans. Theclustering circuit 104 may be part of the adaptive intelligent systems158 and may use any of a wide range of clustering models and techniques,such as ones that are based on attributes of entities 198 that arecollected by the monitoring systems 164 or data collection systems 166and/or stored in the data storage system 186.

In embodiments the loan for which collateral is aggregated may be any ofan auto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments clustering the collateral is performed by a clusteringalgorithm that groups collateral based on attributes collected by thedata collection and monitoring services.

In embodiments attributes used for grouping are selected from among atype of item, a category of item, a specification of an item, a productfeature set of an item, a model of item, a brand of item, a manufacturerof item, a status of item, a context of item, a state of item, a valueof item, a storage location of item, a geolocation of item, an age ofitem, a maintenance history of item, a usage history of item, anaccident history of item, a fault history of item, an ownership of item,an ownership history of item, a price of a type of item, a value of atype of item, an assessment of an item, and a valuation of an item.

In embodiments the set of smart contract services allocates a group ofsimilar items as collateral across a set of loans among differentparties, thereby diversifying risk across the loans.

In embodiments the platform or system may further include a set ofvaluation services that uses a valuation model to set a value forcollateral based on information from the data collection and monitoringservices, wherein the set of smart contract services automaticallyrebalances items of collateral for a set of loans based on the value ofthe collateral.

In embodiments a set of similar collateral items for a set of loans isaggregated in real time based on a similarity in status of the set ofitems.

In embodiments the similarity in status is based on the items being intransit during a defined time period.

In embodiments a set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

Smart Contract that Manages, in a Blockchain and Distributed Ledger, aLien on an Asset Based on Status of a Loan for which the Asset isCollateral

In embodiments, provided herein is a smart contract system for managinga lien on collateral for a loan having a set of computational services.In embodiments, the platform or system includes (a) a set of datacollection and monitoring services for monitoring the status of a loanand an associated set of items of collateral for the loan; (b) a set ofblockchain services for maintaining a secure historical ledger of eventsrelated to the loan, the blockchain services having access controlfeatures that govern access by a set of parties involved in a loan; and(c) a set of smart contract services for managing a smart lendingcontract, wherein the set of smart contract services processesinformation from the set of data collection and monitoring services andautomatically at least one of initiates and terminates a lien on atleast one item in the set of collateral based on the status of the loan,wherein the action on the lien is recorded in the distributed ledger forthe loan.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the status of the loan is determined based on the statusof at least one of an entity related to the loan and a state ofperformance of a condition for the loan.

In embodiments the performance of a condition relates to at least one ofa payment performance and satisfaction of a covenant.

In embodiments the set of data collection and monitoring servicesmonitors an entity to determine compliance with a covenant.

In embodiments the entity is a party, and the set of data collection andmonitoring services monitors the financial condition of an entity thatis a party to the loan.

In embodiments the financial condition is determined based on a set ofattributes of the entity selected from among a publicly stated valuationof the entity, a set of property owned by the entity as indicated bypublic records, a valuation of a set of property owned by the entity, abankruptcy condition of an entity, a foreclosure status of an entity, acontractual default status of an entity, a regulatory violation statusof an entity, a criminal status of an entity, an export controls statusof an entity, an embargo status of an entity, a tariff status of anentity, a tax status of an entity, a credit report of an entity, acredit rating of an entity, a web site rating of an entity, a set ofcustomer reviews for a product of an entity, a social network rating ofan entity, a set of credentials of an entity, a set of referrals of anentity, a set of testimonials for an entity, a set of behavior of anentity, a location of an entity, and a geolocation of an entity.

In embodiments the party is selected from among a primary lender, asecondary lender, a lending syndicate, a corporate lender, a governmentlender, a bank lender, a secured lender, bond issuer, a bond purchaser,an unsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, and an accountant.

In embodiments the entity is a set of collateral for the loan and theset of data collection and monitoring services monitor the status of thecollateral.

In embodiments the set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the platform or system may further include a set ofvaluation services that uses a valuation model to set a value for a setof collateral based on information from the data collection andmonitoring services.

In embodiments the set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the set of valuation services includes artificialintelligence services that iteratively improve the valuation model basedon outcome data relating to transactions in collateral.

In embodiments the set of valuation services further includes a set ofmarket value data collection services that monitor and report onmarketplace information relevant to the value of collateral.

In embodiments the set of market value data collection services monitorspricing or financial data for items that are similar to the collateralin at least one public marketplace.

In embodiments a set of similar items for valuing an item of collateralis constructed using a similarity clustering algorithm based on theattributes of the collateral.

In embodiments the attributes are selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral and ageolocation of the collateral.

In embodiments terms and conditions for the loan that are specified andmanaged by the set of smart contract services is selected from among aprincipal amount of debt, a balance of debt, a fixed interest rate, avariable interest rate, a payment amount, a payment schedule, a balloonpayment schedule, a specification of collateral, a specification ofsubstitutability of collateral, a party, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, and a consequence of default.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

Smart Contract/Blockchain that Allows Substitution of Collateral for aLoan Based on Validated Information about the Collateral (Ownership,Condition, Value)

In embodiments, provided herein is a smart contract system for managingcollateral for a loan having a set of computational services. Inembodiments, the platform or system includes (a) a set of datacollection and monitoring services for monitoring the status of a loanand of an associated set of items of collateral for the loan; (b) a setof blockchain services for maintaining a secure historical ledger ofevents related to the loan, the blockchain services having accesscontrol features that govern access by a set of parties involved in aloan; and (c) a set of smart contract services for managing a smartlending contract, wherein the set of smart contract services processesinformation from the set of data collection and monitoring services andautomatically initiates at least one of substitution, removal, oraddition of a set of items to the set of collateral for the loan basedon an outcome of the processing, wherein the change in the set ofcollateral is recorded in the distributed ledger for the loan.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the status of the loan is determined based on the statusof at least one of an entity related to the loan and a state ofperformance of a condition for the loan.

In embodiments the performance of a condition relates to at least one ofa payment performance and satisfaction of a covenant.

In embodiments the set of data collection and monitoring servicesmonitors an entity to determine compliance with a covenant.

In embodiments the entity is a party, and the set of data collection andmonitoring services monitors the financial condition of an entity thatis a party to the loan.

In embodiments the financial condition is determined based on a set ofattributes of the entity selected from among a publicly stated valuationof the entity, a set of property owned by the entity as indicated bypublic records, a valuation of a set of property owned by the entity, abankruptcy condition of an entity, a foreclosure status of an entity, acontractual default status of an entity, a regulatory violation statusof an entity, a criminal status of an entity, an export controls statusof an entity, an embargo status of an entity, a tariff status of anentity, a tax status of an entity, a credit report of an entity, acredit rating of an entity, a web site rating of an entity, a set ofcustomer reviews for a product of an entity, a social network rating ofan entity, a set of credentials of an entity, a set of referrals of anentity, a set of testimonials for an entity, a set of behavior of anentity, a location of an entity, and a geolocation of an entity.

In embodiments the party is selected from among a primary lender, asecondary lender, a lending syndicate, a corporate lender, a governmentlender, a bank lender, a secured lender, bond issuer, a bond purchaser,an unsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, and an accountant.

In embodiments the entity is a set of collateral for the loan and theset of data collection and monitoring services monitors the status ofthe collateral.

In embodiments the set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the platform or system may further include a set ofvaluation services that uses a valuation model to set a value for a setof collateral based on information from the data collection andmonitoring services.

In embodiments the smart contract initiates substitution, removal oraddition of collateral items to the set of collateral for the loan tomaintain a value of collateral within a stated range.

In embodiments the set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the set of valuation services includes artificialintelligence services that iteratively improve the valuation model basedon outcome data relating to transactions in collateral.

In embodiments the set of valuation services further includes a set ofmarket value data collection services that monitor and report onmarketplace information relevant to the value of collateral.

In embodiments the set of market value data collection services monitorspricing or financial data for items that are similar to the collateralin at least one public marketplace.

In embodiments a set of similar items for valuing an item of collateralis constructed using a similarity clustering algorithm based on theattributes of the collateral.

In embodiments the attributes are selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral and ageolocation of the collateral.

In embodiments terms and conditions for the loan that are specified andmanaged by the set of smart contract services is selected from among aprincipal amount of debt, a balance of debt, a fixed interest rate, avariable interest rate, a payment amount, a payment schedule, a balloonpayment schedule, a specification of collateral, a specification ofsubstitutability of collateral, a party, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, and a consequence of default.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

In embodiments a lending platform is provided having a smart contractthat automatically adjusts an interest rate for a loan based on at leastone of a regulatory factor and a market factor for a specificjurisdiction.

Referring to FIG. 55, in embodiments a lending platform is providedhaving a crowdsourcing system for obtaining information about at leastone of a state of a set of collateral for a loan and a state of anentity relevant to a guarantee for a loan. Thus, in embodiments, aplatform is provided herein, with systems, methods, processes, services,components and other elements for enabling a blockchain and smartcontract platform 5500 for crowdsourcing information relevant tolending. As with other embodiments described above in connection withsourcing innovation, product demand, or the like, a blockchain 136, suchas optionally embodying a distributed ledger, may be configured with aset of smart contracts to administer a reward 512 for the submission ofloan information 518, such as evidence of ownership of property,evidence of title, information about ownership of collateral,information about condition of collateral, information about thelocation of collateral, information about a party's identity,information about a party's creditworthiness, information about aparty's activities or behavior, information about a party's businesspractices, information about the status of performance of a contract,information about accounts receivable, information about accountspayable, information about the value of collateral, and many other typesof information. In embodiments, a blockchain 136, such as optionallydistributed in a distributed ledger, may be used to configure a requestfor loan information 518 along with terms and conditions 510 related tothe information, such as a reward 512 for submission of the loaninformation 518, a set of terms and conditions 510 related to the use ofthe loan information 518), and various parameters 508, such as timingparameters, the nature of the information required (such asindependently validated information like title records, video footage,photographs, witnessed statements, or the like), and other parameters508.

The platform 5500 may include a crowdsourcing interface 520, which maybe included in or provided in coordination with a website, application,dashboard, communications system (such as for sending emails, texts,voice messages, advertisements, broadcast messages, or other message),by which a message may be presented in the crowdsourcing interface 520or sent to relevant individuals (whether targeted, such as in the caseof a request to a particular individual, or broadcast, such as toindividuals in a given location, company, organization, or the like)with an appropriate link to the smart contract and associated blockchain136, such that a reply message submitting information 518, with relevantattachments, links, or other information, can be automaticallyassociated (such as via an API 112 or data integration system) with theblockchain 136, such that the blockchain 136, and any optionallyassociated distributed ledger, maintains a secure, definitive record ofinformation 518 submitted in response to the request. Where a reward 512is offered, the blockchain 136 and/or smart contract may be used torecord time of submission, the nature of the submission, and the partysubmitting, such that at such time as a submission satisfies theconditions for a reward 512 (such as, for example, upon completion of aloan transaction in which the information 518 was useful), theblockchain 136 and any distributed ledger stored thereby can be used toidentify the submitter and, by execution of the smart contract, conveythe reward 512 (which may take any of the forms of consideration notedthroughout this disclosure. In embodiments, the blockchain 136 and anyassociated ledger may include identifying information for submissions ofinformation 518 without containing actual information 518, such thatinformation may be maintained secret (such as being encrypted or beingstored separately with only identifying information), subject tosatisfying or verifying conditions for access (such as identification orverification of a person who has legitimate access rights, such as by anidentity or security application 148). Rewards 512 may be provided basedon outcomes of cases or situations to which information 518 relates,based on a set of rules (which may be automatically applied in somecases, such as using a smart contract in concert with an automationsystem, a rule processing system, an artificial intelligence system 156or other expert system, which in embodiments may comprise one that istrained on a training data set created with human experts. For example,a machine vision system may be used to evaluate evidence of theexistence and/or condition of collateral based on images of items, andparties submitting information about collateral may be rewarded, such asvia tokens or other consideration, via distribution of rewards 512through the smart contract, blockchain 136 and any distributed ledger.Thus, the platform 500 may be used for a wide variety of fact-gatheringand information-gathering purposes, to facilitate validation ofcollateral, to validate representations about behavior, to validateoccurrence of conditions of compliance, to validate occurrence ofconditions of default, to deter improper behavior or misrepresentations,to reduce uncertainty, to reduce asymmetries of information, or thelike.

In embodiments, information may relate to fact-gathering ordata-gathering for a variety of applications and solutions that may besupported by a lending enablement platform 100, including thecrowdsourcing system 520, such as for an underwriting solution 103(e.g., of various types of loans, guarantees, and other items), riskmanagement solutions 122 (such as managing a wide variety of risks notedthroughout this disclosure, such as risks associated with individualloans, packages of loans, tranches of loans and the like); lendingapplications 144 (such as evidence of the ownership and or value ofcollateral, evidence of the veracity of representations, evidence ofperformance or compliance with loan covenants, and the like); regulatoryand/or compliance solutions 142 (such as with respect to compliance witha wide range of regulations that may govern entities 198 and processes,behaviors or activities of or by entities 198); and fraud preventionapplications 138 (such as to detect fraud, misrepresentation, improperbehavior, libel, slander, and the like). For example, a capital loan fora building may include a covenant regarding the use of the property,such as permitting certain uses and prohibiting others, permitting agiven occupancy, or the like, and the crowdsourcing system 520 maysolicit and provide consideration for compliance information about thebuilding (e.g., requesting confirmation from the crowd that a buildingis in fact being used for its intended use as permitted by zoneregulations). Crowdsourced information may be combined with informationfrom monitoring systems 164. In embodiments, an adaptive intelligentsystem 158 may, for example, continuously monitor a property, an item ofcollateral 102 or other entity 198 and, upon recognition (such as by anAI system, such as a neural network classifier) of a suspicious event(e.g., one that may indicate violation of a loan covenant), the adaptiveintelligent system 158 may provide a signal to the crowdsourcing system520 indicating that a crowdsourcing process should be initiated toverify the presence or absence of the violation. In embodiments, thismay include classifying the covenant-related condition that using amachine classifier, providing the classification along with identifyingdata about an entity, and automatically configuring, such as based on amodel or set of rules, a crowdsource request that identifies whatinformation is requested about what entity 198 and what reward 512 isprovided. In embodiment, rewards 512 may be configured by experts,rewards 512 may be based on a set of rules (such as ones that operate onparameters of the loan, the terms and conditions of a covenant in asmart contract (such as loan value, remaining term, and the like), thevalue of collateral 102, or the like), and/or reward 512 may be set byrobotic process automation (RPA) 154, such as where an RPA 154 system istrained on a training set of expert activities in setting rewards invarious contexts that collectively show what rewards are appropriate ingiven situations. Robotic process automation (RPA) 154 of rewardconfiguration may be continuously improved by artificial intelligence156, such as based on a continuous feedback of outcomes ofcrowdsourcing, such as outcomes of success (e.g., verification ofcovenant defaults, yield outcomes, and the like).

Information gathering may include information gathering with respect toentities 198 and their identities, assertions, claims, actions orbehaviors, among many other factors and may be accomplished bycrowdsourcing in the platform 500 or by data collection systems 166 andmonitoring systems 164, optionally with automation via robotic processautomation (RPA) 154 and adaptive intelligence, such as using anartificial intelligence system 156.

Referring to FIG. 6, a platform-operated marketplace crowdsourcingsystem 500 may be configured, such as in a crowdsourcing dashboardinterface 618 or other user interface for an operator of theplatform-operated marketplace crowdsourcing system 120, using thevarious enabling capabilities of the lending enablement platform 100described throughout this disclosure. The operator may use the userinterface or dashboard 514 to undertake a series of steps to perform orundertake an algorithm to create a crowdsourcing request for information518 as described in connection with FIG. 5. In embodiments, one or moreof the steps of the algorithm to create a reward 512 within thedashboard 514 may include, at a step 602, identifying potential rewards512, such as what information 518 is likely to be of value in a givensituation (such as may be indicated through various communicationchannels by stakeholders or representatives of an entity, such as anindividual or enterprise, such as attorneys, agents, investigators,parties, auditors, detectives, underwriters, inspectors, and manyothers).

The dashboard 514 may be configured with a crowdsourcing dashboardinterface 618, such as with elements (including application programmingelements, data integration elements, messaging elements, and the like)that allow a crowdsourcing request to be managed in the platformmarketplace 500 and/or in one or more external marketplaces 188. In thedashboard 514, at a step 604 the user may configure one or moreparameters 508 or conditions 510, such as comprising or describing theconditions (of the type described herein) for the crowdsourcing request,such as by defining a set of conditions 510 that trigger the reward 512and determine allocation of the reward 512 to a set of submitters ofinformation 518. The user interface of the dashboard 514, which mayinclude or be associated with the crowdsourcing dashboard interface 620,may include a set of drop down menus, tables, forms, or the like withdefault, templated, recommended, or pre-configured conditions,parameters 508, conditions 510 and the like, such as ones that areappropriate for various types of crowdsourcing requests. Once theconditions and other parameters of the request are configured, at a step608 a smart contract and blockchain 136 may be configured to maintain,such as via a ledger, the data required to provision, allocate, andexchange data related to the request and to submissions of information518. The smart contract and blockchain 136 may be configured to identityinformation 518, transaction information (such as for exchanges ofinformation), technical information, other evidence data of the typedescribed in connection with FIG. 5, including any data, testimony,photo or video content or other information that may be relevant to asubmission of information 518 or the conditions 510 for a reward 512. Ata step 610 a smart contract may be configured to embody the conditions510 that were configured at the step 604 and to operate on theblockchain 136 that was created at the step 608, as well as to operateon other data, such as data indicating facts, conditions, events, or thelike in the platform-operated marketplace 500 and/or an externalmarketplace 188 or other information site or resource, such as onesrelated to submission information 518, such as sites indicating outcomesof legal cases or portions of cases, sites reporting on investigations,and the like. The smart contract may be configured at the step 610 toapply one or more rules, execute one or more conditional operations, orthe like upon data, such as evidence data 518 and data indicatingsatisfaction of parameters 508 or conditions 510, as well as identitydata, transactional data, timing data, and other data. Onceconfiguration of one or more blockchains 136 and one or more smartcontracts is complete, at a step 612 the blockchain 136 and smartcontract may be deployed in the platform-operated marketplace 500,external marketplace 188 or other site or environment, such as forinteraction by one or more submitters or other users, who may, such asin a crowdsourcing dashboard interface 620, such as a website,application, or the like, enter into the smart contract, such as bysubmitting a submission of information 518 and requesting the reward512, at which point the platform 500, such as using the adaptiveintelligent systems 158 or other capabilities, may store relevant data,such as submission data information 518, identity data for the party orparties entering the smart contract on the blockchain 136 or otherwiseon the platform 500. At a step 614, once the smart contract is executed,the platform 500 may monitor, such as by the monitoring systems 164layer, the platform-operated marketplace 500 and/or one or more externalmarketplaces 188 or other sites for submission data information 518,event data 176, or other data that may satisfy or indicate satisfactionof one or more conditions 510 or trigger application of one or morerules of the smart contract, such as to trigger a reward 512.

At a step 616, upon satisfaction of conditions 510, smart contracts maybe settled, executed, or the like, resulting updates or other operationson the blockchain 136, such as by transferring consideration (such asvia a payments system) and transferring access to information 518. Thus,via the above-referenced steps, an operator of the platform-operatedmarketplace 500 may discover, configure, deploy and have executed a setof smart contracts that crowdsource information relevant to a loan (suchas information about value or condition of collateral 102, compliancewith covenants, fraud or misrepresentation, and the like) and that arecryptographically secured and transferred on a blockchain 136 frominformation gatherers to parties seeking information. In embodiments,the adaptive intelligent systems 158 layer may be used to monitor thesteps of the algorithm described above, and one or more artificialintelligence systems may be used to automate, such as by robotic processautomation (RPA) 154, the entire process or one or more sub-steps orsub-algorithms. This may occur as described above, such as by having anartificial intelligence system 156 learn on a training set of dataresulting from observations, such as monitoring software interactions ofhuman users as they undertake the above-referenced steps. Once trained,the adaptive intelligent systems 158 layer may thus enable the lendingenablement platform 100 to provide a fully automated platform forcrowdsourcing of loan information.

Crowdsourcing System for Validating Quality, Title, or Other Conditionsof Collateral for a Loan

In embodiments, provided herein is a crowdsourcing system for validatingconditions of collateral 102 or assets 218 for a loan. In embodiments,the platform or system includes (a) a set of crowdsourcing services bywhich a crowdsourcing request is communicated to a group of informationsuppliers and by which responses to the request are collected andprocessed to provide a reward to at least one successful informationsupplier; (b) an interface to the set of crowdsourcing services thatenables configuration of parameters of the request, wherein the requestand parameters are configured to obtain information related to thecondition of a set of collateral for a loan; and (c) a set of publishingservices that publish the crowdsourcing request.

In embodiments the reward is managed by a smart contract that processesresponses to the crowdsourcing request and automatically allocates areward to information that satisfies a set of parameters configured forthe crowdsourcing request.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments condition of collateral 102 or assets 218 includescondition attributes selected from the group consisting of the qualityof the collateral, the condition of the collateral, the status of titleto the collateral, the status of possession of the collateral, thestatus of a lien on the collateral, a new or used status of item, a typeof item, a category of item, a specification of an item, a productfeature set of an item, a model of item, a brand of item, a manufacturerof item, a status of item, a context of item, a state of item, a valueof item, a storage location of item, a geolocation of item, an age ofitem, a maintenance history of item, a usage history of item, anaccident history of an item, a fault history of an item, an ownership ofan item, an ownership history of an item, a price of a type of item, avalue of a type of item, an assessment of an item, and a valuation of anitem.

In embodiments the platform or system may further include a set ofblockchain services that record identifying information and parametersof the request, responses to the crowdsourcing request, and rewards in adistributed ledger for the crowdsourcing request.

In embodiments the interface is a graphical user interface configured toenable a workflow by which a human user enters parameters to establishthe crowdsourcing request.

In embodiments the parameters include a type of requested information, areward, and a condition for receiving the reward.

In embodiments the parameter is a reward, and the reward is selectedfrom among a financial reward, a token, a ticket, a contractual right, acryptocurrency, a set of reward points, a currency, a discount on aproduct or service, and an access right.

In embodiments the platform or system may further include a set of smartcontract services 134 that administer a smart lending contract, whereinthe smart contract services 134 process information from the set ofcrowdsourcing services and automatically undertake an action related tothe loan.

In embodiments the action is at least one of a foreclosure action, alien administration action, an interest-rate setting action, a defaultinitiation action, a substitution of collateral, and a calling of theloan.

In embodiments the platform or system may further include a roboticprocess automation system (RPA) 154 that is trained, based on a trainingset of interactions of human users with the interface to the set ofcrowdsourcing services, to configure a crowdsourcing request based on aset of attributes of a loan. In embodiments the attributes of the loanare obtained from a set of smart contract services that manage the loan.In embodiments the robotic process automation system is configured to beiteratively trained and improved based on a set of outcomes from a setof crowdsourcing requests. In embodiments training includes training therobotic process automation system to set a reward. In embodimentstraining includes training the robotic process automation system todetermine a set of domains to which the request will be published. Inembodiments training includes training the robotic process automationsystem to configure the content of a request.

Crowdsourcing System for Validating the Quality of a Personal Guaranteefor a Loan

In embodiments, provided herein is a crowdsourcing system 520 forvalidating conditions of collateral 102 or assets 218 for a loan. Inembodiments, the platform or system includes (a) a set of crowdsourcingservices by which a crowdsourcing request is communicated to a group ofinformation suppliers and by which responses to the request arecollected and processed to provide a reward to at least one successfulinformation supplier; (b) an interface to the set of crowdsourcingservices that enables configuration of parameters of the request,wherein the request and parameters are configured to obtain informationrelated to the condition of guarantor for a loan; and (c) a set ofpublishing services that publish the crowdsourcing request.

In embodiments the set of crowdsourcing systems 520 obtains informationabout the financial condition of an entity that is the guarantor for theloan.

In embodiments the financial condition is determined at least in partbased on information about the entity selected from among a publiclystated valuation of the entity, a set of property owned by the entity asindicated by public records, a valuation of a set of property owned bythe entity, a bankruptcy condition of an entity, a foreclosure status ofan entity, a contractual default status of an entity, a regulatoryviolation status of an entity, a criminal status of an entity, an exportcontrols status of an entity, an embargo status of an entity, a tariffstatus of an entity, a tax status of an entity, a credit report of anentity, a credit rating of an entity, a web site rating of an entity, aset of customer reviews for a product of an entity, a social networkrating of an entity, a set of credentials of an entity, a set ofreferrals of an entity, a set of testimonials for an entity, a set ofbehavior of an entity, a location of an entity, and a geolocation of anentity.

In embodiments the reward is managed by a smart contract that processesresponses to the crowdsourcing request and automatically allocates areward to information that satisfies a set of parameters configured forthe crowdsourcing request.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the platform or system may further include an interfaceof the crowdsourcing services In embodiments a request is configured toobtain information about condition of a set of collateral for the loan,wherein the set of collateral items is selected from among a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments condition of collateral includes condition attributesselected from the group consisting of the quality of the collateral, thecondition of the collateral, the status of title to the collateral, thestatus of possession of the collateral, the status of a lien on thecollateral, a new or used status of item, a type of item, a category ofitem, a specification of an item, a product feature set of an item, amodel of item, a brand of item, a manufacturer of item, a status ofitem, a context of item, a state of item, a value of item, a storagelocation of item, a geolocation of item, an age of item, a maintenancehistory of item, a usage history of item, an accident history of anitem, a fault history of an item, an ownership of an item, an ownershiphistory of an item, a price of a type of item, a value of a type ofitem, an assessment of an item, and a valuation of an item.

In embodiments the platform or system may further include a set ofblockchain services that record identifying information and parametersof the request, responses to the crowdsourcing request, and rewards in adistributed ledger for the crowdsourcing request.

In embodiments the interface is a graphical user interface configured toenable a workflow by which a human user enters parameters to establishthe crowdsourcing request.

In embodiments the parameters include a type of requested information, areward, and a condition for receiving the reward.

In embodiments the parameter is a reward, and the reward is selectedfrom among a financial reward, a token, a ticket, a contractual right, acryptocurrency, a set of reward points, a currency, a discount on aproduct or service, and an access right.

In embodiments the platform or system may further include a set of smartcontract services that administer a smart lending contract, wherein thesmart contract services process information from the set ofcrowdsourcing services and automatically undertake an action related tothe loan.

In embodiments the action is at least one of a foreclosure action, alien administration action, an interest-rate setting action, a defaultinitiation action, a substitution of collateral, and a calling of theloan.

In embodiments the platform or system may further include a roboticprocess automation system that is trained, based on a training set ofinteractions of human users with the interface to the set ofcrowdsourcing services, to configure a crowdsourcing request based on aset of attributes of a loan.

In embodiments the attributes of the loan are obtained from a set ofsmart contract services that manage the loan.

In embodiments the robotic process automation system is configured to beiteratively trained and improved based on a set of outcomes from a setof crowdsourcing requests.

In embodiments training includes training the robotic process automationsystem to set a reward, to determine a set of domains to which therequest will be published or to configure the content of a request.

Referring to FIG. 7, in embodiments a lending platform is providedhaving smart contract services 134 that automatically adjusts aninterest rate for a loan based on information collected via at least oneof an Internet of Things system, a crowdsourcing system, a set of socialnetwork analytic services and a set of data collection and monitoringservices. The lending enablement platform 100 may include an interestrate automation solution 224 that may include a set of interfaces,workflows, and models (which may include, use or be enabled by variousadaptive intelligent systems 158) and other components that areconfigured to enable automation of the setting of interest rates basedon a set of conditions, which may include smart contract terms andconditions, marketplace conditions (of platform marketplaces and/orexternal marketplaces 188, conditions monitored by monitoring systems164 and data collection systems 166, and the like (such as of entities198, including without limitation parties 210, collateral 102 and assets218, among others). For example, a user of the interest rate automationsolution 224 may set (such as in a user interface) rules, thresholds,model parameters, and the like that determine, or recommend, an interestrate for a loan based on the above, such as based on interest ratesavailable to the lender from secondary lenders, risk factors of theborrower (including predicted risk based on one or more predictivemodels using artificial intelligence 156), or the system mayautomatically recommend or set such rules, thresholds, parameters andthe like (optionally by learning to do so based on a training set ofoutcomes over time). Interest rates may be determined based on marketingfactors (such as competing interest rates offered by other lenders).Interest rates may be calculated for new loans, for modifications ofexisting loans, for refinancing, for foreclosure situations (e.g.,changing from secured loan rates to unsecured loan rates), and the like.

In embodiments, provided herein is a smart contract system for modifyinga loan having a set of computational services. In embodiments, theplatform or system includes (a) a set of data collection and monitoringservices for monitoring a set of entities involved in a loan; and (b) aset of smart contract services for managing a smart lending contract,wherein the set of smart contract services processes information fromthe set of data collection and monitoring services and automaticallyinitiates a change in an interest rate for the loan based on theinformation.

In embodiments the change in interest rate is based on the condition ofa set of collateral for the loan that is monitored by the set of datacollection and monitoring services.

In embodiments the change in interest rate is based on an attribute of aparty that is monitored by the set of data collection and monitoringservices.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the platform or system may further include a set ofvaluation services that uses a valuation model to set a value for a setof collateral based on information from the data collection andmonitoring services.

In embodiments the change in interest rate is based on the valuation ofa set of collateral for the loan that is monitored by the set of datacollection and monitoring services.

In embodiments a set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the set of valuation services includes artificialintelligence services that iteratively improve the valuation model basedon outcome data relating to transactions in collateral.

In embodiments the set of valuation services further includes a set ofmarket value data collection services that monitor and report onmarketplace information relevant to the value of collateral.

In embodiments the set of market value data collection services monitorspricing or financial data for items that are similar to the collateralin at least one public marketplace.

In embodiments a set of similar items for valuing an item of collateralis constructed using a similarity clustering algorithm based on theattributes of the collateral.

In embodiments the attributes are selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral and ageolocation of the collateral.

In embodiments, provided herein is a smart contract system for modifyinga loan having a set of computational services. In embodiments, theplatform or system includes (a) a set of data collection and monitoringservices for monitoring public sources of information about a set ofentities involved in a loan, wherein the public sources of informationare selected from among website information, news article information,social network information and crowdsourced information; and (b) a setof smart contract services for managing a smart lending contract,wherein the set of smart contract services processes information fromthe set of data collection and monitoring services and automaticallyinitiates a change in an interest rate for the loan based on theinformation.

In embodiments the set of data collection and monitoring servicesmonitor the financial condition of an entity that is a party to theloan.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the financial condition is determined based on a set ofattributes of the entity selected from among a publicly stated valuationof the entity, a set of property owned by the entity as indicated bypublic records, a valuation of a set of property owned by the entity, abankruptcy condition of an entity, a foreclosure status of an entity, acontractual default status of an entity, a regulatory violation statusof an entity, a criminal status of an entity, an export controls statusof an entity, an embargo status of an entity, a tariff status of anentity, a tax status of an entity, a credit report of an entity, acredit rating of an entity, a web site rating of an entity, a set ofcustomer reviews for a product of an entity, a social network rating ofan entity, a set of credentials of an entity, a set of referrals of anentity, a set of testimonials for an entity, a set of behavior of anentity, a location of an entity, and a geolocation of an entity.

In embodiments the party is selected from among a primary lender, asecondary lender, a lending syndicate, a corporate lender, a governmentlender, a bank lender, a secured lender, bond issuer, a bond purchaser,an unsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, and an accountant.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of items of collateral and undertakes anaction related to a loan to which the collateral is subject.

In embodiments the loan-related action is selected from among offering aloan, accepting a loan, underwriting a loan, setting an interest ratefor a loan, deferring a payment requirement, modifying an interest ratefor a loan, validating title for collateral, recording a change intitle, assessing the value of collateral, initiating inspection ofcollateral, calling a loan, closing a loan, setting terms and conditionsfor a loan, providing notices required to be provided to a borrower,foreclosing on property subject to a loan, and modifying terms andconditions for a loan.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

In embodiments the monitored entity is a set of collateral items that isselected from among a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments, provided herein is a smart contract system for modifyinga loan, the system having a set of computational services. Inembodiments, the platform or system includes (a) a set of datacollection and monitoring services for monitoring a set of entitiesinvolved in a loan In embodiments the entities are located in aplurality of different jurisdictions; and (b) a set of smart contractservices for managing a smart lending contract, wherein the set of smartcontract services processes location information about the entities fromthe set of data collection and monitoring services and automaticallyundertakes a loan-related action for the loan based at least in part onthe location information.

In embodiments the loan-related action is selected from among offering aloan, accepting a loan, underwriting a loan, setting an interest ratefor a loan, deferring a payment requirement, modifying an interest ratefor a loan, validating title for collateral, recording a change intitle, assessing the value of collateral, initiating inspection ofcollateral, calling a loan, closing a loan, setting terms and conditionsfor a loan, providing notices required to be provided to a borrower,foreclosing on property subject to a loan, and modifying terms andconditions for a loan.

In embodiments the smart contract is configured to process a set ofjurisdiction-specific regulatory notice requirements and to provide anappropriate notice to a borrower based on location of at least one ofthe lender, the borrower, the funds provided via the loan, the repaymentof the loan, and the collateral for the loan.

In embodiments the smart contract is configured to process a set ofjurisdiction-specific regulatory foreclosure requirements and to providean appropriate foreclosure notice to a borrower based on jurisdiction ofat least one of the lender, the borrower, the funds provided via theloan, the repayment of the loan, and the collateral for the loan.

In embodiments the smart contract is configured to process a set ofjurisdiction-specific rules for setting terms and conditions of the loanand to configure the smart contract based on the location of at leastone of the borrower, the funds provided via the loan, the repayment ofthe loan, and the collateral for the loan.

In embodiments the smart contract is configured to set the interest ratefor the loan to cause the loan to comply with maximum interest ratelimitations applicable in a jurisdiction.

In embodiments the change in interest rate is based on the condition ofa set of collateral for the loan that is monitored by the set of datacollection and monitoring services.

In embodiments the change in interest rate is based on an attribute of aparty that is monitored by the set of data collection and monitoringservices.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the platform or system may further include a set ofvaluation services that uses a valuation model to set a value for a setof collateral based on information from the data collection andmonitoring services.

In embodiments the valuation model is a jurisdiction-specific valuationmodel that is based on the jurisdiction of at least one of the lender,the borrower, the delivery of funds provided via loan, the payment ofthe loan and collateral for the loan.

In embodiments at least one of the terms and conditions for the loan isbased on the valuation of a set of collateral for the loan that ismonitored by the set of data collection and monitoring services.

In embodiments a set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the set of valuation services includes artificialintelligence services that iteratively improve the valuation model basedon outcome data relating to transactions in collateral.

In embodiments the set of valuation services further includes a set ofmarket value data collection services that monitor and report onmarketplace information relevant to the value of collateral.

In embodiments the set of market value data collection services monitorspricing or financial data for items that are similar to the collateralin at least one public marketplace.

In embodiments a set of similar items for valuing an item of collateralis constructed using a similarity clustering algorithm based on theattributes of the collateral.

In embodiments the attributes are selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral and ageolocation of the collateral.

Referring to FIG. 8, in embodiments a lending platform is providedhaving a smart contract that automatically restructures debt based on amonitored condition. The lending enablement platform 100 may include adebt restructuring solution 228 that may include a set of interfaces,workflows, and models (which may include, use or be enabled by variousadaptive intelligent systems 158) and other components that areconfigured to enable automation of the restructuring of debt based on aset of conditions, which may include smart contract terms andconditions, marketplace conditions (of platform marketplaces and/orexternal marketplaces 188, conditions monitored by monitoring systems164 and data collection systems 166, and the like (such as of entities198, including without limitation parties 210, collateral 102 and assets218, among others). For example, a user of the debt restructuringsolution 228 may create, configure (such as using one or more templatesor libraries), modify, set or otherwise handle (such as in a userinterface of the debt restructuring solution 228) various rules,thresholds, procedures, workflows, model parameters, and the like thatdetermine, or recommend, a debt restructuring action for a loan based onone or more events, conditions, states, actions, or the like, whererestructuring may be based on various factors, such as prevailing marketinterest rates, interest rates available to the lender from secondarylenders, risk factors of the borrower (including predicted risk based onone or more predictive models using artificial intelligence 156), statusof other debt (such as new debt of a borrower, elimination of debt of aborrower, or the like), condition of collateral 102 or assets 218 usedto secure or back a loan, state of a business or business operation(e.g., receivables, payables, or the like), and many others.Restructuring may include changes in interest rate, changes in priorityof secured parties, changes in collateral 102 or assets 218 used to backor secure debt, changes in parties, changes in guarantors, changes inpayment schedule, changes in principal balance (e.g., includingforgiveness or acceleration of payments), and others. In embodiments thedebt restructuring solution 228 may automatically recommend or set suchrules, thresholds, actions, parameters and the like (optionally bylearning to do so based on a training set of outcomes over time),resulting in a recommended restructuring plan, which may specify aseries of actions required to accomplish a recommended restructuring,which may be automated and may be involved conditional execution ofsteps based on monitored conditions and/or smart contract terms, whichmay be created, configured, and/or accounted for by the debtrestructuring plan. Restructuring plans may be determined and executedbased at least one part on market factors (such as competing interestrates offered by other lenders, values of collateral, and the like) aswell as regulatory and/or compliance factors. Restructuring plans may begenerated and/or executed for modifications of existing loans, forrefinancing, for foreclosure situations (e.g., changing from securedloan rates to unsecured loan rates), for bankruptcy or insolvencysituations, for situations involving market changes (e.g., changes inprevailing interest rates) and others. In embodiments, adaptiveintelligent systems 158, including artificial intelligence 156 may betrained on a training set of restructuring activities by experts and/oron outcomes of restructuring actions to generate a set of predictions,classifications, control instructions, plans, models, or the like forautomated creation, management and/or execution of one or more aspectsof a restructuring plan.

In embodiments, provided herein is a smart contract system for modifyinga loan, the system having a set of computational services. Inembodiments, the platform or system includes (a) a set of datacollection and monitoring services for monitoring a set of entitiesinvolved in a loan; and (b) a set of smart contract services formanaging a smart lending contract, wherein the set of smart contractservices processes information from the set of data collection andmonitoring services and automatically restructures debt based on amonitored condition.

In embodiments the restructuring is based on the condition of a set ofcollateral for the loan that is monitored by the set of data collectionand monitoring services.

In embodiments the restructuring is according to a set of rules that arebased on a covenant of the loan, wherein the restructuring occurs uponan event that is determined with respect to at least one of themonitored entities that relates to the covenant.

In embodiments the event is the failure of collateral for a loan toexceed a required fractional value of the remaining balance of the loan.

In embodiments the event is a default of the buyer with respect to aloan covenant.

In embodiments the restructuring is based on an attribute of a partythat is monitored by the set of data collection and monitoring services.

In embodiments the set of smart contract services further includesservices for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions, loan-related eventsand loan-related activities.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the set of terms and conditions for the loan that arespecified and managed by the set of smart contract services is selectedfrom among a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the platform or system may further include a set ofvaluation services that uses a valuation model to set a value for a setof collateral based on information from the data collection andmonitoring services.

In embodiments the restructuring of the debt is based on the valuationof a set of collateral for the loan that is monitored by the set of datacollection and monitoring services.

In embodiments a set of collateral items is selected from among avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments the set of valuation services includes artificialintelligence services that iteratively improve the valuation model basedon outcome data relating to transactions in collateral.

In embodiments the set of valuation services further includes a set ofmarket value data collection services that monitor and report onmarketplace information relevant to the value of collateral.

In embodiments the set of market value data collection services monitorspricing or financial data for items that are similar to the collateralin at least one public marketplace.

In embodiments a set of similar items for valuing an item of collateralis constructed using a similarity clustering algorithm based on theattributes of the collateral.

In embodiments the attributes are selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral and ageolocation of the collateral.

Referring to FIG. 9, in embodiments a lending enablement platform 100 isprovided having a social network analytics application 204 formonitoring social media, collecting data and determining analytics forvalidating the reliability of a guarantee for a loan. The lendingenablement platform 100 may include a guarantee and/or securitymonitoring solution 230 that may include a set of interfaces, workflows,and models (which may include, use or be enabled by various adaptiveintelligent systems 158) and other components that are configured toenable monitoring of a guarantee and/or security for a lendingtransaction based on a set of conditions, which may include smartcontract terms and conditions, marketplace conditions (of platformmarketplaces and/or external marketplaces 188, conditions monitored bymonitoring systems 164 and data collection systems 166, and the like(such as of entities 198, including without limitation parties 210,collateral 102 and assets 218, among others). For example, a user of theguarantee and/or security monitoring solution 230 may set (such as in auser interface) rules, thresholds, model parameters, and the like thatdetermine, or recommend, a monitoring plan for lending transaction suchas based on risk factors of the borrower, risk factors of the lender,market risk factors, and/or risk factors of collateral 102 or assets 218(including predicted risk based on one or more predictive models usingartificial intelligence 156), or the lending enablement platform 100 mayautomatically recommend or set such rules, thresholds, parameters andthe like (optionally by learning to do so based on a training set ofoutcomes over time). The guarantee and/or security monitoring solution230 may configure a set of social network analytics services 204 and/orother monitoring systems 164 and/or data collection systems 166 tosearch, parse, extract, and process data from one or more socialnetworks, website, or the like, such as ones that may containinformation about collateral 102 or assets 218 (e.g., photos that show avehicle, boat, or other personal property of a party 210, photos of ahome or other real property, photos or text that describes activities ofa party 210 (including ones that indicate financial risk, physical risk,health risk, or other risk that may be relevant to the quality of theguarantor and/or the guarantee for a payment obligation and/or theability of the borrower to repay a loan when due). For example, a photoshowing a borrower driving a regular passenger vehicle in off-roadconditions may be flagged as indicating that the vehicle cannot be fullyrelied upon as collateral for an automobile loan that has a highremaining balance.

Thus, in embodiments, provided herein is a social network monitoringsystem for validating conditions of a guarantee for a loan. Inembodiments, the platform or system includes (a) a set of social networkdata collection and monitoring services by which data is collected by aset of algorithms that are configured to monitor social networkinformation about entities involved in a loan; and (b) an interface tothe set of social networking services that enables configuration ofparameters of the social network data collection and monitoring servicesto obtain information related to the condition of guarantee.

In embodiments the set of social network data collection and monitoringservices obtains information about the financial condition of an entitythat is the guarantor for the loan.

In embodiments the financial condition is determined at least in partbased on information contained in a social network about the entityselected from among a publicly stated valuation of the entity, a set ofproperty owned by the entity as indicated by public records, a valuationof a set of property owned by the entity, a bankruptcy condition of anentity, a foreclosure status of an entity, a contractual default statusof an entity, a regulatory violation status of an entity, a criminalstatus of an entity, an export controls status of an entity, an embargostatus of an entity, a tariff status of an entity, a tax status of anentity, a credit report of an entity, a credit rating of an entity, aweb site rating of an entity, a set of customer reviews for a product ofan entity, a social network rating of an entity, a set of credentials ofan entity, a set of referrals of an entity, a set of testimonials for anentity, a set of behavior of an entity, a location of an entity, and ageolocation of an entity.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the platform or system may further include an interfaceof the social network data collection and monitoring services Inembodiments the data collection and monitoring service is configured toobtain information about condition of a set of collateral for the loan,wherein the set of collateral items is selected from among a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments condition of collateral includes condition attributesselected from the group consisting of the quality of the collateral, thecondition of the collateral, the status of title to the collateral, thestatus of possession of the collateral, the status of a lien on thecollateral, a new or used status of item, a type of item, a category ofitem, a specification of an item, a product feature set of an item, amodel of item, a brand of item, a manufacturer of item, a status ofitem, a context of item, a state of item, a value of item, a storagelocation of item, a geolocation of item, an age of item, a maintenancehistory of item, a usage history of item, an accident history of anitem, a fault history of an item, an ownership of an item, an ownershiphistory of an item, a price of a type of item, a value of a type ofitem, an assessment of an item, and a valuation of an item.

In embodiments the interface is a graphical user interface configured toenable a workflow by which a human user enters parameters to establishthe social network data collection and monitoring request.

In embodiments the platform or system may further include a set of smartcontract services that administer a smart lending contract, wherein thesmart contract services process information from the set of socialnetwork data collection and monitoring services and automaticallyundertake an action related to the loan.

In embodiments the action is at least one of a foreclosure action, alien administration action, an interest-rate setting action, a defaultinitiation action, a substitution of collateral, and a calling of theloan.

In embodiments the platform or system may further include a roboticprocess automation system that is trained, based on a training set ofinteractions of human users with the interface to the set of socialnetwork data collection and monitoring services, to configure a datacollection and monitoring action based on a set of attributes of a loan.

In embodiments the attributes of the loan are obtained from a set ofsmart contract services that manage the loan.

In embodiments the robotic process automation system is configured to beiteratively trained and improved based on a set of outcomes from a setof social network data collection and monitoring requests.

In embodiments training includes training the robotic process automationsystem to determine a set of domains to which the social network datacollection and monitoring services will applied.

In embodiments training includes training the robotic process automationsystem to configure the content of a social network data collection andmonitoring search.

Referring still to FIG. 9, in embodiments a lending platform is providedhaving an Internet of Things data collection and monitoring system forvalidating reliability of a guarantee for a loan. The guarantee and/orsecurity monitoring solution 230 may include the capability to use datafrom, and configure collection activities by, a set of Internet ofThings services 208 (which may include various IoT devices, edgedevices, edge computation and processing capabilities, and the like asdescribed in connection with various embodiments), such as ones thatmonitor various entities 198 and their environments involved in lendingtransactions.

In embodiments, provided herein is a monitoring system for validatingconditions of a guarantee for a loan. For example, a set of algorithmsmay be configured to initiate data collection by IoT devices, to managedata collection, and the like such as based on the conditions referencedabove, including conditions that relate to risk factors of the borroweror lender, market risk factors, physical risk factors, or the like. Forexample, an IoT system may be configured to capture video or images of ahome during periods of bad weather, such as to determine whether thehome is at risk of a flood, wind damage, or the like, in order toconfirm whether the home can be predicted to serve as adequatecollateral for a home loan, a line of credit, or other lendingtransaction.

In embodiments, the platform or system includes (a) a set of Internet ofThings data collection and monitoring services by which data iscollected by a set of algorithms that are configured to monitor Internetof Things information collected from and about entities involved in aloan; and (b) an interface to the set of Internet of Things datacollection and monitoring services that enables configuration ofparameters of the social network data collection and monitoring servicesto obtain information related to the condition of guarantee.

In embodiments the set of Internet of Things data collection andmonitoring services obtains information about the financial condition ofan entity that is the guarantor for the loan.

In embodiments the financial condition is determined at least in partbased on information collected by an Internet of Things device about theentity selected from among a publicly stated valuation of the entity, aset of property owned by the entity as indicated by public records, avaluation of a set of property owned by the entity, a bankruptcycondition of an entity, a foreclosure status of an entity, a contractualdefault status of an entity, a regulatory violation status of an entity,a criminal status of an entity, an export controls status of an entity,an embargo status of an entity, a tariff status of an entity, a taxstatus of an entity, a credit report of an entity, a credit rating of anentity, a website rating of an entity, a set of customer reviews for aproduct of an entity, a social network rating of an entity, a set ofcredentials of an entity, a set of referrals of an entity, a set oftestimonials for an entity, a set of behavior of an entity, a locationof an entity, and a geolocation of an entity.

In embodiments the loan is of at least one type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the platform or system may further include an interfaceof the set of Internet of Things data collection and monitoring servicesIn embodiments the set of data collection and monitoring services isconfigured to obtain information about condition of a set of collateralfor the loan, wherein the set of collateral items is selected from amonga vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, and an item of personalproperty.

In embodiments condition of collateral includes condition attributesselected from the group consisting of the quality of the collateral, thecondition of the collateral, the status of title to the collateral, thestatus of possession of the collateral, the status of a lien on thecollateral, a new or used status of item, a type of item, a category ofitem, a specification of an item, a product feature set of an item, amodel of item, a brand of item, a manufacturer of item, a status ofitem, a context of item, a state of item, a value of item, a storagelocation of item, a geolocation of item, an age of item, a maintenancehistory of item, a usage history of item, an accident history of anitem, a fault history of an item, an ownership of an item, an ownershiphistory of an item, a price of a type of item, a value of a type ofitem, an assessment of an item, and a valuation of an item.

In embodiments the interface is a graphical user interface configured toenable a workflow by which a human user enters parameters to establishan Internet of Things data collection and monitoring services monitoringaction.

In embodiments the platform or system may further include a set of smartcontract services that administer a smart lending contract, wherein theset of smart contract services process information from the set ofInternet of Things data collection and monitoring services andautomatically undertakes an action related to the loan.

In embodiments the action is at least one of a foreclosure action, alien administration action, an interest-rate setting action, a defaultinitiation action, a substitution of collateral, and a calling of theloan.

In embodiments the platform or system may further include a roboticprocess automation system that is trained, based on a training set ofinteractions of human users with the interface to the set of Internet ofThings data collection and monitoring services, to configure a datacollection and monitoring action based on a set of attributes of a loan.

In embodiments the attributes of the loan are obtained from a set ofsmart contract services that manage the loan.

In embodiments the robotic process automation system is configured to beiteratively trained and improved based on a set of outcomes from a setof Internet of Things data collection and monitoring servicesactivities.

In embodiments training includes training the robotic process automationsystem to determine a set of domains to which the Internet of Thingsdata collection and monitoring services will applied.

In embodiments training includes training the robotic process automationsystem to configure the content of Internet of Things data collectionand monitoring services activities.

Referring to FIG. 10, in embodiments a lending platform is providedhaving a robotic process automation system (RPA) 154 for negotiation ofa set of terms and conditions for a loan. The RPA system 154 may provideautomation for one or more aspects of a negotiation solution 232 thatenables automated negotiation and/or provides a recommendation or planfor a negotiation relevant to a lending transaction. The negotiationsolution 232 and/or RPA system 154 for negotiation may include a set ofinterfaces, workflows, and models (which may include, use or be enabledby various adaptive intelligent systems 158) and other components thatare configured to enable automation of one or more aspects of anegotiation of one or more terms and conditions of a lendingtransaction, such as based on a set of conditions, which may includesmart contract terms and conditions, marketplace conditions (of platformmarketplaces and/or external marketplaces 188, conditions monitored bymonitoring systems 164 and data collection systems 166, and the like(such as of entities 198, including without limitation parties 210,collateral 102 and assets 218, among others). For example, a user of thenegotiation solution 232 may create, configure (such as using one ormore templates or libraries), modify, set or otherwise handle (such asin a user interface of the negotiation solution 232 and/or RPA system154) various rules, thresholds, conditional procedures, workflows, modelparameters, and the like that determine, or recommend, a negotiationaction or plan for a lending transaction negotiation based on one ormore events, conditions, states, actions, or the like, where thenegotiation plan may be based on various factors, such as prevailingmarket interest rates, interest rates available to the lender fromsecondary lenders, risk factors of the borrower, the lender, one or moreguarantors, market risk factors and the like (including predicted riskbased on one or more predictive models using artificial intelligence156), status of debt, condition of collateral 102 or assets 218 used tosecure or back a loan, state of a business or business operation (e.g.,receivables, payables, or the like), conditions of parties 210 (such asnet worth, wealth, debt, location, and other conditions), behaviors ofparties (such as behaviors indicating preferences, behaviors indicatingnegotiation styles), and many others. Negotiation may includenegotiation of lending transaction terms and conditions, debtrestructuring, foreclosure activities, setting interest rates, changesin interest rate, changes in priority of secured parties, changes incollateral 102 or assets 218 used to back or secure debt, changes inparties, changes in guarantors, changes in payment schedule, changes inprincipal balance (e.g., including forgiveness or acceleration ofpayments), and many other transactions or terms and conditions. Inembodiments the negotiation solution 232 may automatically recommend orset rules, thresholds, actions, parameters and the like (optionally bylearning to do so based on a training set of outcomes over time),resulting in a recommended negotiation plan, which may specify a seriesof actions required to accomplish a recommended or desired outcome ofnegotiation (such as within a range of acceptable outcomes), which maybe automated and may involve conditional execution of steps based onmonitored conditions and/or smart contract terms, which may be created,configured, and/or accounted for by the negotiation plan. Negotiationplans may be determined and executed based at least one part on marketfactors (such as competing interest rates offered by other lenders,values of collateral, and the like) as well as regulatory and/orcompliance factors. Negotiation plans may be generated and/or executedfor creation of new loans, for creation of guarantees and security, forsecondary loans, for modifications of existing loans, for refinancing,for foreclosure situations (e.g., changing from secured loan rates tounsecured loan rates), for bankruptcy or insolvency situations, forsituations involving market changes (e.g., changes in prevailinginterest rates) and others. In embodiments, adaptive intelligent systems158, including artificial intelligence 156 may be trained on a trainingset of negotiation activities by experts and/or on outcomes ofnegotiation actions to generate a set of predictions, classifications,control instructions, plans, models, or the like for automated creation,management and/or execution of one or more aspects of a negotiationplan.

In embodiments, provided herein is a robotic process automation systemfor negotiating a loan. In embodiments, the platform or system includes(a) a set of data collection and monitoring services for collecting atraining set of interactions among entities for a set of loantransactions; (b) an artificial intelligence system that is trained onthe training set of interactions to classify a set of loan negotiationactions; and (c) a robotic process automation system that is trained ona set of loan transaction interactions and a set of loan transactionoutcomes to negotiate the terms and conditions of a loan on behalf of aparty to a loan.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the entities are a set of parties to a loan transaction.

In embodiments the set of parties is selected from among a primarylender, a secondary lender, a lending syndicate, a corporate lender, agovernment lender, a bank lender, a secured lender, bond issuer, a bondpurchaser, an unsecured lender, a guarantor, a provider of security, aborrower, a debtor, an underwriter, an inspector, an assessor, anauditor, a valuation professional, a government official, and anaccountant.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the robotic process automation is trained on a set ofinteractions of parties with a set of user interfaces involved in a setof lending processes.

In embodiments upon completion of negotiation a smart contract for aloan is automatically configured by a set of smart contract servicesbased on the outcome of the negotiation.

In embodiments at least one of an outcome and a negotiating event of thenegotiation is recorded in a distributed ledger associated with theloan.

In embodiments the loan is of a type selected from among an auto loan,an inventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments, provided herein is a robotic process automation systemfor negotiating refinancing of a loan. In embodiments, the platform orsystem includes (a) a set of data collection and monitoring services forcollecting a training set of interactions between entities for a set ofloan refinancing activities; an artificial intelligence system that istrained on the training set of interactions to classify a set of loanrefinancing actions; and (c) a robotic process automation system that istrained on a set of loan refinancing interactions and a set of loanrefinancing outcomes to undertake a loan refinancing activity on behalfof a party to a loan.

In embodiments the loan refinancing activity includes initiating anoffer to refinance, initiating a request to refinance, configuring arefinancing interest rate, configuring a refinancing payment schedule,configuring a refinancing balance, configuring collateral for arefinancing, managing use of proceeds of a refinancing, removing orplacing a lien associated with a refinancing, verifying title for arefinancing, managing an inspection process, populating an application,negotiating terms and conditions for a refinancing and closing arefinancing.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the entities are a set of parties to a loan transaction.

In embodiments the set of parties is selected from among a primarylender, a secondary lender, a lending syndicate, a corporate lender, agovernment lender, a bank lender, a secured lender, bond issuer, a bondpurchaser, an unsecured lender, a guarantor, a provider of security, aborrower, a debtor, an underwriter, an inspector, an assessor, anauditor, a valuation professional, a government official, and anaccountant.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the robotic process automation is trained on a set ofinteractions of parties with a set of user interfaces involved in a setof lending processes.

In embodiments upon completion of a refinancing process a smart contractfor a refinance loan is automatically configured by a set of smartcontract services based on the outcome of the refinancing activity.

In embodiments at least one of an outcome and an event of therefinancing is recorded in a distributed ledger associated with therefinancing loan.

In embodiments the loan is of a type selected from among an auto loan,an inventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

Referring to FIG. 11, in embodiments a lending platform is providedhaving a robotic process automation system for loan collection. The RPAsystem 154 may provide automation for one or more aspects of acollection solution 238 that enables automated collection and/orprovides a recommendation or plan for a collection activity relevant toa lending transaction. The collection solution 238 and/or RPA system 154for collection may include a set of interfaces, workflows, and models(which may include, use or be enabled by various adaptive intelligentsystems 158) and other components that are configured to enableautomation of one or more aspects of a collection action of one or moreterms and conditions of a collection process for a lending transaction,such as based on a set of conditions, which may include smart contractterms and conditions, marketplace conditions (of platform marketplacesand/or external marketplaces 188, conditions monitored by monitoringsystems 164 and data collection systems 166, and the like (such as ofentities 198, including without limitation parties 210, collateral 102and assets 218, among others). For example, a user of the collectionsolution 238 may create, configure (such as using one or more templatesor libraries), modify, set or otherwise handle (such as in a userinterface of the collection solution 238 and/or RPA system 154) variousrules, thresholds, conditional procedures, workflows, model parameters,and the like that determine, or recommend, a collection action or planfor a lending transaction or loan monitoring solution based on one ormore events, conditions, states, actions, or the like, where thecollection plan may be based on various factors, such as the status ofpayments, the status of the borrower, the status of collateral 102 orassets 218, risk factors of the borrower, the lender, one or moreguarantors, market risk factors and the like (including predicted riskbased on one or more predictive models using artificial intelligence156), status of debt, condition of collateral 102 or assets 218 used tosecure or back a loan, state of a business or business operation (e.g.,receivables, payables, or the like), conditions of parties 210 (such asnet worth, wealth, debt, location, and other conditions), behaviors ofparties (such as behaviors indicating preferences, behaviors indicatinghow borrowers respond to communication styles, communication cadence,and the like), and many others. Collection may include collection withrespect to loans, communications to encourage payments, and the like. Inembodiments the collection solution 238 may automatically recommend orset rules, thresholds, actions, parameters and the like (optionally bylearning to do so based on a training set of outcomes over time),resulting in a recommended collection plan, which may specify a seriesof actions required to accomplish a recommended or desired outcome ofcollection (such as within a range of acceptable outcomes), which may beautomated and may involve conditional execution of steps based onmonitored conditions and/or smart contract terms, which may be created,configured, and/or accounted for by the collection plan. Collectionplans may be determined and executed based at least one part on marketfactors (such as competing interest rates offered by other lenders,values of collateral, and the like) as well as regulatory and/orcompliance factors. Collection plans may be generated and/or executedfor creation of new loans, for secondary loans, for modifications ofexisting loans, for refinancing, for foreclosure situations (e.g.,changing from secured loan rates to unsecured loan rates), forbankruptcy or insolvency situations, for situations involving marketchanges (e.g., changes in prevailing interest rates) and others. Inembodiments, adaptive intelligent systems 158, including artificialintelligence 156 may be trained on a training set of collectionactivities by experts and/or on outcomes of collection actions togenerate a set of predictions, classifications, control instructions,plans, models, or the like for automated creation, management and/orexecution of one or more aspects of a collection plan.

In embodiments, provided herein is a robotic process automation systemfor handling collection of a loan. In embodiments, the platform orsystem includes (a) a set of data collection and monitoring services forcollecting a training set of interactions among entities for a set ofloan transactions that involve collection of a set of payments for a setof loans; (b) an artificial intelligence system that is trained on thetraining set of interactions to classify a set of loan collectionactions; and (c) a robotic process automation system that is trained ona set of loan transaction interactions and a set of loan collectionoutcomes to undertake a loan collection action on behalf of a party to aloan.

In embodiments the loan collection action undertaken by the roboticprocess automation system is selected from among initiation of acollection process, referral of a loan to an agent for collection,configuration of a collection communication, scheduling of a collectioncommunication, configuration of content for a collection communication,configuration of an offer to settle a loan, termination of a collectionaction, deferral of a collection action, configuration of an offer foran alternative payment schedule, initiation of a litigation, initiationof a foreclosure, initiation of a bankruptcy process, a repossessionprocess, and placement of a lien on collateral.

In embodiments the set of loan collection outcomes is selected fromamong a response to a collection contact event, a payment of a loan, adefault of the borrower on a loan, a bankruptcy of a borrower of a loan,an outcome of a collection litigation, a financial yield of a set ofcollection actions, a return on investment on collection and a measureof reputation of a party involved in collection.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities. In embodiments the entities are set of partiesto a loan transaction. In embodiments the set of parties is selectedfrom among a primary lender, a secondary lender, a lending syndicate, acorporate lender, a government lender, a bank lender, a secured lender,bond issuer, a bond purchaser, an unsecured lender, a guarantor, aprovider of security, a borrower, a debtor, an underwriter, aninspector, an assessor, an auditor, a valuation professional, agovernment official, and an accountant.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the robotic process automation is trained on a set ofinteractions of parties with a set of user interfaces involved in a setof lending processes.

In embodiments upon completion of negotiation of a collection process asmart contract for a loan is automatically configured by a set of smartcontract services based on the outcome of the negotiation.

In embodiments at least one of a collection outcome and a collectionevent is recorded in a distributed ledger associated with the loan.

In embodiments the loan is of a type selected from among an auto loan,an inventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

Referring to FIG. 12, in embodiments a lending platform is providedhaving a robotic process automation system for consolidating a set ofloans. The RPA system 154 may provide automation for one or more aspectsof a consolidation solution 240 that enables automated consolidationand/or provides a recommendation or plan for a consolidation activityrelevant to a lending transaction. The consolidation solution 240 and/orRPA system 154 for consolidation may include a set of interfaces,workflows, and models (which may include, use or be enabled by variousadaptive intelligent systems 158) and other components that areconfigured to enable automation of one or more aspects of aconsolidation action or a consolidation process for a lendingtransaction, such as based on a set of conditions, which may includesmart contract terms and conditions, marketplace conditions (of platformmarketplaces and/or external marketplaces 188, conditions monitored bymonitoring systems 164 and data collection systems 166, and the like(such as of entities 198, including without limitation parties 210,collateral 102 and assets 218, among others). For example, a user of theconsolidation solution 240 may create, configure (such as using one ormore templates or libraries), modify, set or otherwise handle (such asin a user interface of the consolidation solution 240 and/or RPA system154) various rules, thresholds, conditional procedures, workflows, modelparameters, and the like that determine, or recommend, a consolidationaction or plan for a lending transaction or a set of loans based on oneor more events, conditions, states, actions, or the like, where theconsolidation plan may be based on various factors, such as the statusof payments, interest rates of the set of loans, prevailing interestrates in a platform marketplace or external marketplace, the status ofthe borrowers of a set of loans, the status of collateral 102 or assets218, risk factors of the borrower, the lender, one or more guarantors,market risk factors and the like (including predicted risk based on oneor more predictive models using artificial intelligence 156), status ofdebt, condition of collateral 102 or assets 218 used to secure or back aset of loans, the state of a business or business operation (e.g.,receivables, payables, or the like), conditions of parties 210 (such asnet worth, wealth, debt, location, and other conditions), behaviors ofparties (such as behaviors indicating preferences, behaviors indicatingdebt preferences), and many others. Consolidation may includeconsolidation with respect to terms and conditions of sets of loans,selection of appropriate loans, configuration of payment terms forconsolidated loans, configuration of payoff plans for pre-existingloans, communications to encourage consolidation, and the like. Inembodiments the consolidation solution 240 may automatically recommendor set rules, thresholds, actions, parameters and the like (optionallyby learning to do so based on a training set of outcomes over time),resulting in a recommended consolidation plan, which may specify aseries of actions required to accomplish a recommended or desiredoutcome of consolidation (such as within a range of acceptableoutcomes), which may be automated and may involve conditional executionof steps based on monitored conditions and/or smart contract terms,which may be created, configured, and/or accounted for by theconsolidation plan. Consolidation plans may be determined and executedbased at least one part on market factors (such as competing interestrates offered by other lenders, values of collateral, and the like) aswell as regulatory and/or compliance factors. Consolidation plans may begenerated and/or executed for creation of new consolidated loans, forsecondary loans related to consolidated loans, for modifications ofexisting loans related to consolidation, for refinancing terms of aconsolidated loan, for foreclosure situations (e.g., changing fromsecured loan rates to unsecured loan rates), for bankruptcy orinsolvency situations, for situations involving market changes (e.g.,changes in prevailing interest rates) and others. In embodiments,adaptive intelligent systems 158, including artificial intelligence 156may be trained on a training set of consolidation activities by expertsand/or on outcomes of consolidation actions to generate a set ofpredictions, classifications, control instructions, plans, models, orthe like for automated creation, management and/or execution of one ormore aspects of a consolidation plan.

In embodiments, provided herein is a robotic process automation systemfor consolidating a set of loans. In embodiments, the platform or systemincludes (a) a set of data collection and monitoring services forcollecting information about a set of loans and for collecting atraining set of interactions between entities for a set of loanconsolidation transactions; (b) an artificial intelligence system thatis trained on the training set of interactions to classify a set ofloans as candidates for consolidation; and (c) a robotic processautomation system that is trained on a set of loan consolidationinteractions to manage consolidation of at least a subset of the set ofloans on behalf of a party to the consolidation.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the set of loans that are classified as candidates forconsolidation are determined based on a model that processes attributesof entities involved in the set of loans, wherein the attributesselected from among identity of a party, interest rate, payment balance,payment terms, payment schedule, type of loan, type of collateral,financial condition of party, payment status, condition of collateral,and value of collateral.

In embodiments managing consolidation includes managing at least one ofidentification of loans from a set of candidate loans, preparation of aconsolidation offer, preparation of a consolidation plan, preparation ofcontent communicating a consolidation offer, scheduling a consolidationoffer, communicating a consolidation offer, negotiating a modificationof a consolidation offer, preparing a consolidation agreement, executinga consolidation agreement, modifying collateral for a set of loans,handling an application workflow for consolidation, managing aninspection, managing an assessment, setting an interest rate, deferringa payment requirement, setting a payment schedule, and closing aconsolidation agreement. In embodiments the entities are a set ofparties to a loan transaction. In embodiments the set of parties isselected from among a primary lender, a secondary lender, a lendingsyndicate, a corporate lender, a government lender, a bank lender, asecured lender, bond issuer, a bond purchaser, an unsecured lender, aguarantor, a provider of security, a borrower, a debtor, an underwriter,an inspector, an assessor, an auditor, a valuation professional, agovernment official, and an accountant.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the robotic process automation is trained on a set ofinteractions of parties with a set of user interfaces involved in a setof consolidation processes. In embodiments upon completion ofnegotiation a smart contract for a consolidated loan is automaticallyconfigured by a set of smart contract services based on the outcome ofthe negotiation. In embodiments at least one of an outcome and anegotiating event of the negotiation is recorded in a distributed ledgerassociated with the loan.

In embodiments the loan is of a type selected from among an auto loan,an inventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

Referring to FIG. 13, in embodiments a lending platform is providedhaving a robotic process automation system for managing a factoringtransaction. The RPA system 154 may provide automation for one or moreaspects of a factoring solution 242 that enables automated factoringand/or provides a recommendation or plan for a factoring activityrelevant to a lending transaction, such as one involving factoring ofreceivables. The factoring solution 242 and/or RPA system 154 forfactoring may include a set of interfaces, workflows, and models (whichmay include, use or be enabled by various adaptive intelligent systems158) and other components that are configured to enable automation ofone or more aspects of a factoring action of one or more terms andconditions of a factoring transaction, such as based on a set ofconditions, which may include smart contract terms and conditions,marketplace conditions (of platform marketplaces and/or externalmarketplaces 188, conditions monitored by monitoring systems 164 anddata collection systems 166, and the like (such as of entities 198,including without limitation parties 210, collateral 102 and assets 218,accounts receivable, and inventory, among others). For example, a userof the factoring solution 242 may create, configure (such as using oneor more templates or libraries), modify, set or otherwise handle (suchas in a user interface of the factoring solution 242 and/or RPA system154) various rules, thresholds, conditional procedures, workflows, modelparameters, and the like that determine, or recommend, a factoringaction or plan for a factoring transaction or monitoring solution basedon one or more events, conditions, states, actions, or the like, wherethe factoring plan may be based on various factors, such as the statusof receivables, the status of work-in-progress, the status of inventory,the status of delivery and/or shipment, the status of payments, thestatus of the borrower, the status of collateral 102 or assets 218, riskfactors of the borrower, the lender, one or more guarantors, market riskfactors and the like (including predicted risk based on one or morepredictive models using artificial intelligence 156), status of debt,condition of collateral 102 or assets 218 used to secure or back a loan,state of a business or business operation (e.g., receivables, payables,or the like), conditions of parties 210 (such as net worth, wealth,debt, location, and other conditions), behaviors of parties (such asbehaviors indicating preferences, behaviors indicating negotiationstyles, and the like), and many others. Factoring may include factoringwith respect to loans, communications to encourage payments, and thelike. In embodiments the factoring solution 242 may automaticallyrecommend or set rules, thresholds, actions, parameters and the like(optionally by learning to do so based on a training set of outcomesover time), resulting in a recommended factoring plan, which may specifya series of actions required to accomplish a recommended or desiredoutcome of factoring (such as within a range of acceptable outcomes),which may be automated and may involve conditional execution of stepsbased on monitored conditions and/or smart contract terms, which may becreated, configured, and/or accounted for by the factoring plan.Factoring plans may be determined and executed based at least one parton market factors (such as competing interest rates or other terms andconditions offered by other lenders, values of collateral, values ofaccounts receivable, interest rates, and the like) as well as regulatoryand/or compliance factors. Factoring plans may be generated and/orexecuted for creation of new factoring arrangements, for modificationsof existing factoring arrangements, and others. In embodiments, adaptiveintelligent systems 158, including artificial intelligence 156 may betrained on a training set of factoring activities by experts and/or onoutcomes of factoring actions to generate a set of predictions,classifications, control instructions, plans, models, or the like forautomated creation, management and/or execution of one or more aspectsof a factoring plan.

In embodiments, provided herein is a robotic process automation systemfor consolidating a set of loans. In embodiments, the platform or systemincludes (a) a set of data collection and monitoring services forcollecting information about entities involved in a set of factoringloans and for collecting a training set of interactions between entitiesfor a set of factoring loan transactions; (b) an artificial intelligencesystem that is trained on the training set of interactions to classifythe entities involved in the set of factoring loans; and (c) a roboticprocess automation system that is trained on the set of factoring loaninteractions to manage a factoring loan.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the artificial intelligence system uses a model thatprocesses attributes of entities involved in the set of factoring loans,wherein the attributes selected from assets used for factoring, identityof a party, interest rate, payment balance, payment terms, paymentschedule, type of loan, type of collateral, financial condition ofparty, payment status, condition of collateral, and value of collateral.

In embodiments the assets used for factoring include a set of accountsreceivable.

In embodiments managing a factoring loan includes managing at least oneof a set of assets for factoring, identification of loans for factoringfrom a set of candidate loans, preparation of a factoring offer,preparation of a factoring plan, preparation of content communicating afactoring offer, scheduling a factoring offer, communicating a factoringoffer, negotiating a modification of a factoring offer, preparing afactoring agreement, executing a factoring agreement, modifyingcollateral for a set of factoring loans, handing transfer of a set ofaccounts receivable, handling an application workflow for factoring,managing an inspection, managing an assessment of a set of assets to befactored, setting an interest rate, deferring a payment requirement,setting a payment schedule, and closing a factoring agreement.

In embodiments the entities are a set of parties to a loan transaction.

In embodiments the set of parties is selected from among a primarylender, a secondary lender, a lending syndicate, a corporate lender, agovernment lender, a bank lender, a secured lender, bond issuer, a bondpurchaser, an unsecured lender, a guarantor, a provider of security, aborrower, a debtor, an underwriter, an inspector, an assessor, anauditor, a valuation professional, a government official, and anaccountant.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the robotic process automation is trained on a set ofinteractions of parties with a set of user interfaces involved in a setof factoring processes.

In embodiments upon completion of negotiation a smart contract for afactoring loan is automatically configured by a set of smart contractservices based on the outcome of the negotiation.

In embodiments at least one of an outcome and a negotiating event of thenegotiation is recorded in a distributed ledger associated with theloan.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

Referring to FIG. 14, in embodiments a lending platform is providedhaving a robotic process automation system for brokering a loan. Theloan may be, for example, a mortgage loan.

The RPA system 154 may provide automation for one or more aspects of abrokering solution 244 that enables automated brokering and/or providesa recommendation or plan for a brokering activity relevant to a lendingtransaction, such as for brokering a set of mortgage loans, home loans,lines of credit, automobile loans, construction loans, or other loans ofany of the types described herein. The brokering solution 244 and/or RPAsystem 154 for brokering may include a set of interfaces, workflows, andmodels (which may include, use or be enabled by various adaptiveintelligent systems 158) and other components that are configured toenable automation of one or more aspects of a brokering action or abrokering process for a lending transaction, such as based on a set ofconditions, which may include smart contract terms and conditions,marketplace conditions (of platform marketplaces and/or externalmarketplaces 188, conditions monitored by monitoring systems 164 anddata collection systems 166, and the like (such as of entities 198,including without limitation parties 210, collateral 102 and assets 218,among others, as well as of interest rates, available lenders, availableterms and the like). For example, a user of the brokering solution 244may create, configure (such as using one or more templates orlibraries), modify, set or otherwise handle (such as in a user interfaceof the brokering solution 244 and/or RPA system 154) various rules,thresholds, conditional procedures, workflows, model parameters, and thelike that determine, or recommend, a brokering action or plan forbrokering a set of loans of a given type or types based on one or moreevents, conditions, states, actions, or the like, where the brokeringplan may be based on various factors, such as the interest rates of theset of loans available from various primary and secondary lenders,permitted attributes of borrowers (e.g., based on income, wealth,location, or the like) prevailing interest rates in a platformmarketplace or external marketplace, the status of the borrowers of aset of loans, the status or other attributes of collateral 102 or assets218, risk factors of the borrower, the lender, one or more guarantors,market risk factors and the like (including predicted risk based on oneor more predictive models using artificial intelligence 156), status ofdebt, condition of collateral 102 or assets 218 available to secure orback a set of loans, the state of a business or business operation(e.g., receivables, payables, or the like), conditions of parties 210(such as net worth, wealth, debt, location, and other conditions),behaviors of parties (such as behaviors indicating preferences,behaviors indicating debt preferences), and many others. Brokering mayinclude brokering with respect to terms and conditions of sets of loans,selection of appropriate loans, configuration of payment terms forconsolidated loans, configuration of payoff plans for pre-existingloans, communications to encourage borrowing, and the like. Inembodiments the brokering solution 244 may automatically recommend orset rules, thresholds, actions, parameters and the like (optionally bylearning to do so based on a training set of outcomes over time),resulting in a recommended brokering plan, which may specify a series ofactions required to accomplish a recommended or desired outcome ofbrokering (such as within a range of acceptable outcomes), which may beautomated and may involve conditional execution of steps based onmonitored conditions and/or smart contract terms, which may be created,configured, and/or accounted for by the brokering plan. Brokering plansmay be determined and executed based at least one part on market factors(such as competing interest rates offered by other lenders, propertyvalues, attributes of borrowers, values of collateral, and the like) aswell as regulatory and/or compliance factors. Brokering plans may begenerated and/or executed for creation of new loans, for secondaryloans, for modifications of existing loans, for refinancing terms, forsituations involving market changes (e.g., changes in prevailinginterest rates or property values) and others. In embodiments, adaptiveintelligent systems 158, including artificial intelligence 156 may betrained on a training set of brokering activities by experts and/or onoutcomes of brokering actions to generate a set of predictions,classifications, control instructions, plans, models, or the like forautomated creation, management and/or execution of one or more aspectsof a brokering plan.

In embodiments, provided herein is a robotic process automation systemfor automating brokering of a mortgage. In embodiments, the platform orsystem includes (a) a set of data collection and monitoring services forcollecting information about entities involved in a set of mortgage loanactivities and for collecting a training set of interactions betweenentities for a set of mortgage loan transactions; (b) an artificialintelligence system that is trained on the training set of interactionsto classify the entities involved in the set of mortgage loans; and (c)a robotic process automation system that is trained on at least one ofthe set of mortgage loan activities and the set of mortgage loaninteractions to broker a mortgage loan.

In embodiments at least one of the set of mortgage loan activities andthe set of mortgage loan interactions includes activities amongmarketing activity, identification of a set of prospective borrowers,identification of property, identification of collateral, qualificationof borrower, title search, title verification, property assessment,property inspection, property valuation, income verification, borrowerdemographic analysis, identification of capital providers, determinationof available interest rates, determination of available payment termsand conditions, analysis of existing mortgage, comparative analysis ofexisting and new mortgage terms, completion of application workflow,population of fields of application, preparation of mortgage agreement,completion of schedule to mortgage agreement, negotiation of mortgageterms and conditions with capital provider, negotiation of mortgageterms and conditions with borrower, transfer of title, placement of lienand closing of mortgage agreement.

In embodiments the set of data collection and monitoring servicesincludes services selected from among a set of Internet of Thingssystems that monitor the entities, a set of cameras that monitor theentities, a set of software services that pull information related tothe entities from publicly available information sites, a set of mobiledevices that report on information related to the entities, a set ofwearable devices worn by human entities, a set of user interfaces bywhich entities provide information about the entities and a set ofcrowdsourcing services configured to solicit and report informationrelated to the entities.

In embodiments the artificial intelligence system uses a model thatprocesses attributes of entities involved in the set of mortgage loans,wherein the attributes are selected from properties that are subject tomortgages, assets used for collateral, identity of a party, interestrate, payment balance, payment terms, payment schedule, type ofmortgage, type of property, financial condition of party, paymentstatus, condition of property, and value of property.

In embodiments managing a mortgage loan includes managing at least oneof a property that is subject to a mortgage, identification of candidatemortgages from a set of borrower situations, preparation of a mortgageoffer, preparation of content communicating a mortgage offer, schedulinga mortgage offer, communicating a mortgage offer, negotiating amodification of a mortgage offer, preparing a mortgage agreement,executing a mortgage agreement, modifying collateral for a set ofmortgage loans, handing transfer of a lien, handling an applicationworkflow, managing an inspection, managing an assessment of a set ofassets to be subject to a mortgage, setting an interest rate, deferringa payment requirement, setting a payment schedule, and closing amortgage agreement. In embodiments the entities are a set of parties toa loan transaction. In embodiments the set of parties is selected fromamong a primary lender, a secondary lender, a lending syndicate, acorporate lender, a government lender, a bank lender, a secured lender,bond issuer, a bond purchaser, an unsecured lender, a guarantor, aprovider of security, a borrower, a debtor, an underwriter, aninspector, an assessor, an auditor, a valuation professional, agovernment official, and an accountant.

In embodiments the artificial intelligence system includes at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the robotic process automation is trained on a set ofinteractions of parties with a set of user interfaces involved in a setof mortgage-related activities. In embodiments upon completion ofnegotiation a smart contract for a mortgage loan is automaticallyconfigured by a set of smart contract services based on the outcome ofthe negotiation. In embodiments at least one of an outcome and anegotiating event of the negotiation is recorded in a distributed ledgerassociated with the loan. In embodiments the artificial intelligencesystem includes at least one of a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, and a simulation system.

Referring to FIG. 15, in embodiments a lending platform is providedhaving a crowdsourcing and automated classification system forvalidating condition of an issuer for a bond. The RPA system 154 mayprovide automation for one or more aspects of a bond management solution234 that enables automated bond management and/or provides arecommendation or plan for a bond management activity relevant to a bondtransaction, such as for municipal bonds, corporate bonds, governmentbonds, or other bonds that may be backed by assets, collateral, orcommitments of a bond issuer. The bond management solution 234 and/orRPA system 154 for bond management may include a set of interfaces,workflows, and models (which may include, use or be enabled by variousadaptive intelligent systems 158) and other components that areconfigured to enable automation of one or more aspects of a bondmanagement action or a management process for a bond transaction, suchas based on a set of conditions, which may include smart contract termsand conditions, marketplace conditions (of platform marketplaces and/orexternal marketplaces 188, conditions monitored by monitoring systems164 and data collection systems 166, and the like (such as of entities198, including without limitation parties 210, collateral 102 and assets218, among others, as well as of interest rates, available lenders,available terms and the like). For example, a user of the bondmanagement solution 234 may create, configure (such as using one or moretemplates or libraries), modify, set or otherwise handle (such as in auser interface of the bond management solution 234 and/or RPA system154) various rules, thresholds, conditional procedures, workflows, modelparameters, and the like that determine, or recommend, a bond managementaction or plan for management a set of bonds of a given type or typesbased on one or more events, conditions, states, actions, or the like,where the bond management plan may be based on various factors, such asthe interest rates available from various primary and secondary lendersor issuers, permitted attributes of issuers and buyers (e.g., based onincome, wealth, location, or the like) prevailing interest rates in aplatform marketplace or external marketplace, the status of the issuersof a set of bonds, the status or other attributes of collateral 102 orassets 218, risk factors of the issuer, one or more guarantors, marketrisk factors and the like (including predicted risk based on one or morepredictive models using artificial intelligence 156), status of debt,condition of collateral 102 or assets 218 available to secure or back aset of bonds, the state of a business or business operation (e.g.,receivables, payables, or the like), conditions of parties 210 (such asnet worth, wealth, debt, location, and other conditions), behaviors ofparties (such as behaviors indicating preferences, behaviors indicatingdebt preferences), and many others. Bond management may includemanagement with respect to terms and conditions of sets of bonds,selection of appropriate bonds, communications to encouragetransactions, and the like. In embodiments the bond management solution234 may automatically recommend or set rules, thresholds, actions,parameters and the like (optionally by learning to do so based on atraining set of outcomes over time), resulting in a recommended bondmanagement plan, which may specify a series of actions required toaccomplish a recommended or desired outcome of bond management (such aswithin a range of acceptable outcomes), which may be automated and mayinvolve conditional execution of steps based on monitored conditionsand/or smart contract terms, which may be created, configured, and/oraccounted for by the bond management plan. Bond management plans may bedetermined and executed based at least one part on market factors (suchas competing interest rates offered by other issuers, property values,attributes of issuers, values of collateral or assets, and the like) aswell as regulatory and/or compliance factors. Bond management plans maybe generated and/or executed for creation of new bonds, for secondaryloans or transactions to back bonds, for modifications of existingbonds, for situations involving market changes (e.g., changes inprevailing interest rates or property values) and others. Inembodiments, adaptive intelligent systems 158, including artificialintelligence 156 may be trained on a training set of bond managementactivities by experts and/or on outcomes of bond management actions togenerate a set of predictions, classifications, control instructions,plans, models, or the like for automated creation, management and/orexecution of one or more aspects of a bond management plan.

In embodiments, provided herein is a platform, consisting of variousservices, components, modules, programs, systems, devices, algorithms,and other elements, for monitoring condition of an issuer for a bond. Inembodiments, the platform or system includes (a) a set of crowdsourcingsystems 520 for collecting information about a set of entities involvedin a set of bond transactions; and (b) a condition classifying systemhaving a model and a set of artificial intelligence services forclassifying the condition of the set of issuers using information fromthe set of crowdsourcing services, wherein the model is trained using atraining data set of outcomes related to the issuers.

In embodiments the set of entities includes entities among a set ofissuers, a set of bonds, a set of parties, and a set of assets.

In embodiments a set of issuers includes at least one of a municipality,a corporation, a contractor, a government entity, a non-governmentalentity, and a non-profit entity.

In embodiments the set of bonds includes at least one of a municipalbond, a government bond, a treasury bond, an asset-backed bond, and acorporate bond.

In embodiments the condition classified by the condition classifyingsystem is among a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition and an entity healthcondition.

In embodiments the set of crowdsourcing services enables a userinterface by which a user may configure a crowdsourcing request forinformation relevant to the condition about the set of issuers.

In embodiments the platform or system may further include a set ofconfigurable data collection and monitoring services for monitoring theissuers that includes at least one of a set of Internet of Thingsdevices, a set of environmental condition sensors, a set of socialnetwork analytic services and a set of algorithms for querying networkdomains.

In embodiments the set of configurable data collection and monitoringservices monitors an environment selected from among a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle.

In embodiments the set of bonds is backed by a set of assets.

In embodiments the set of assets includes assets among municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of the assets and undertakes an actionrelated to a debt transaction to which the asset is related.

In embodiments the action is selected from among offering a debttransaction, underwriting a debt transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating debt, andconsolidating debt.

In embodiments the artificial intelligence services include at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the platform or system may further include an automatedbond management system that manages an action related to the bond,wherein the automated bond management system is trained on a trainingset of bond management activities.

In embodiments the automated bond management system is trained on a setof interactions of parties with a set of user interfaces involved in aset of bond transaction activities.

In embodiments the set of bond transaction activities includesactivities among offering a debt transaction, underwriting a debttransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating title, managing inspection,recording a change in title, assessing the value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating debt, and consolidating debt.

In embodiments the platform or system may further include a market valuedata collection service that monitors and reports on marketplaceinformation relevant to the value of at least one of the issuer and aset of assets.

In embodiments reporting is on a set of assets that includes at leastone of a municipal asset, a vehicle, a ship, a plane, a building, ahome, real estate property, undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

In embodiments the market value data collection service monitors pricingor financial data for items that are similar to the assets in at leastone public marketplace.

In embodiments a set of similar items for valuing the assets isconstructed using a similarity clustering algorithm based on theattributes of the assets.

In embodiments the attributes are selected from among a category of theassets, asset age, asset condition, asset history, asset storage, andgeolocation of assets.

In embodiments the platform or system may further include a set of smartcontract services for managing a smart contract for the bondtransaction.

In embodiments the smart contract services set terms and conditions forthe bond.

In embodiments the set of terms and conditions for the debt transactionthat are specified and managed by the set of smart contract services isselected from among a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a specification of assetsthat back the bond, a specification of substitutability of assets, aparty, an issuer, a purchaser, a guarantee, a guarantor, a security, apersonal guarantee, a lien, a duration, a covenant, a foreclosecondition, a default condition, and a consequence of default.

In embodiments the lending platform is provided having a social networkmonitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments, provided herein is a platform, consisting of variousservices, components, modules, programs, systems, devices, algorithms,and other elements, for monitoring condition of an issuer for a bond. Inembodiments, the platform or system includes (a) a set of social networkanalytics applications 204 for collecting information about a set ofentities involved in a set of bond transactions; and (b) a conditionclassifying system having a model and a set of artificial intelligenceservices for classifying the condition of the set of issuers based oninformation from the set of social network monitoring and analyticservices, wherein the model is trained using a training data set ofoutcomes related to the issuers.

In embodiments the set of entities includes entities among a set ofissuers, a set of bonds, a set of parties, and a set of assets.

In embodiments a set of issuers includes at least one of a municipality,a corporation, a contractor, a government entity, a non-governmentalentity, and a non-profit entity.

In embodiments the set of bonds includes at least one of a municipalbond, a government bond, a treasury bond, an asset-backed bond, and acorporate bond.

In embodiments the condition classified by the condition classifyingsystem is among a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition and an entity healthcondition.

In embodiments the set of social network monitoring and analyticservices enables a user interface by which a user may configure a queryfor information about the set of entities.

In embodiments the platform or system may further include a set of datacollection and monitoring services for monitoring the entities thatincludes at least one of a set of Internet of Things devices, a set ofenvironmental condition sensors, a set of crowdsourcing services, and aset of algorithms for querying network domains.

In embodiments the set of data collection and monitoring servicesmonitors an environment selected from among a municipal environment, acorporate environment, a securities trading environment, a real propertyenvironment, a commercial facility, a warehousing facility, atransportation environment, a manufacturing environment, a storageenvironment, a home, and a vehicle.

In embodiments the set of bonds is backed by a set of assets. Inembodiments the set of assets includes assets among municipal asset, avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of the assets and undertakes an actionrelated to a bond transaction to which the asset is related.

In embodiments the action is selected from among offering a bondtransaction, underwriting a bond transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating bonds, andconsolidating bonds.

In embodiments the artificial intelligence services include at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the platform or system may further include an automatedbond management system that manages an action related to the bond,wherein the automated bond management system is trained on a trainingset of bond management activities.

In embodiments the automated bond management system is trained on a setof interactions of parties with a set of user interfaces involved in aset of bond transaction activities.

In embodiments the set of bond transaction activities includesactivities among offering a bond transaction, underwriting a bondtransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating title, managing inspection,recording a change in title, assessing the value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating bonds, and consolidating bonds.

In embodiments the platform or system may further include a market valuedata collection service that monitors and reports on marketplaceinformation relevant to the value of at least one of the issuer, a setof bonds, and a set of assets.

In embodiments reporting is on a set of assets that includes at leastone of a municipal asset, a vehicle, a ship, a plane, a building, ahome, real estate property, undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

In embodiments the market value data collection service monitors pricingor financial data for items that are similar to the assets in at leastone public marketplace.

In embodiments a set of similar items for valuing the assets isconstructed using a similarity clustering algorithm based on theattributes of the assets.

In embodiments the attributes are selected from among a category of theassets, asset age, asset condition, asset history, asset storage, andgeolocation of assets.

In embodiments the platform or system may further include a set of smartcontract services for managing a smart contract for the bondtransaction.

In embodiments the smart contract services set terms and conditions forthe bond.

In embodiments the set of terms and conditions for the debt transactionthat are specified and managed by the set of smart contract services isselected from among a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a specification of assetsthat back the bond, a specification of substitutability of assets, aparty, an issuer, a purchaser, a guarantee, a guarantor, a security, apersonal guarantee, a lien, a duration, a covenant, a foreclosecondition, a default condition, and a consequence of default.

In embodiments a lending platform is provided having an Internet ofThings data collection and monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments, provided herein is a platform, consisting of variousservices, components, modules, programs, systems, devices, algorithms,and other elements, for monitoring condition of an issuer for a bond. Inembodiments, the platform or system includes (a) a set of Internet ofThings data collection and monitoring services for collectinginformation about a set of entities involved in a set of bondtransactions; and (b) a condition classifying system having a model anda set of artificial intelligence services for classifying the conditionof the set of issuers based on information from IoT data collectionservices 208, wherein the model is trained using a training data set ofoutcomes related to the issuers.

In embodiments the set of entities includes entities among a set ofissuers, a set of bonds, a set of parties, and a set of assets.

In embodiments a set of issuers includes at least one of a municipality,a corporation, a contractor, a government entity, a non-governmentalentity, and a non-profit entity.

In embodiments the set of bonds includes at least one of a municipalbond, a government bond, a treasury bond, an asset-backed bond, and acorporate bond.

In embodiments the condition classified by the condition classifyingsystem is among a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition and an entity healthcondition.

In embodiments the set of Internet of Things data collection andmonitoring services enables a user interface by which a user mayconfigure a query for information about the set of entities.

In embodiments the platform or system may further include a set ofconfigurable data collection and monitoring services for monitoring theentities that includes at least one of a set of social network analyticservices, a set of environmental condition sensors, a set ofcrowdsourcing services, and a set of algorithms for querying networkdomains.

In embodiments the set of configurable data collection and monitoringservices monitors an environment selected from among a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle.

In embodiments the set of bonds is backed by a set of assets.

In embodiments the set of assets includes assets among municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of the assets and undertakes an actionrelated to a bond transaction to which the asset is related.

In embodiments the action is selected from among offering a bondtransaction, underwriting a bond transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating bonds, andconsolidating bonds.

In embodiments the artificial intelligence services include at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the platform or system may further include an automatedbond management system that manages an action related to the bond,wherein the automated bond management system is trained on a trainingset of bond management activities.

In embodiments the automated bond management system is trained on a setof interactions of parties with a set of user interfaces involved in aset of bond transaction activities.

In embodiments the set of bond transaction activities includesactivities among offering a bond transaction, underwriting a bondtransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating title, managing inspection,recording a change in title, assessing the value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating bonds, and consolidating bonds.

In embodiments the platform or system may further include a market valuedata collection service that monitors and reports on marketplaceinformation relevant to the value of at least one of the issuer, a setof bonds, and a set of assets.

In embodiments reporting is on a set of assets that includes at leastone of a municipal asset, a vehicle, a ship, a plane, a building, ahome, real estate property, undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

In embodiments the market value data collection service monitors pricingor financial data for items that are similar to the assets in at leastone public marketplace.

In embodiments a set of similar items for valuing the assets isconstructed using a similarity clustering algorithm based on theattributes of the assets.

In embodiments the attributes are selected from among a category of theassets, asset age, asset condition, asset history, asset storage, andgeolocation of assets.

In embodiments the platform or system may further include a set of smartcontract services for managing a smart contract for the bondtransaction.

In embodiments the smart contract services set terms and conditions forthe bond.

In embodiments the set of terms and conditions for the debt transactionthat are specified and managed by the set of smart contract services isselected from among a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a specification of assetsthat back the bond, a specification of substitutability of assets, aparty, an issuer, a purchaser, a guarantee, a guarantor, a security, apersonal guarantee, a lien, a duration, a covenant, a foreclosecondition, a default condition, and a consequence of default.

In embodiments, provided herein is a platform, consisting of variousservices, components, modules, programs, systems, devices, algorithms,and other elements, for monitoring condition of an entity and managingdebt related to the entity. In embodiments, the platform or systemincludes (a) a set of data collection and monitoring services forcollecting information about entities involved in a set of debttransactions; (b) a condition classifying system having a model and aset of artificial intelligence services for classifying the condition ofthe set of entities, wherein the model is trained using a training dataset of outcomes related to the entities; and

(c) an automated debt management system that manages an action relatedto the debt, wherein the automated debt management system is trained ona training set of debt management activities.

In embodiments the data collection and monitoring services includes atleast one of a set of Internet of Things devices, a set of environmentalcondition sensors, a set of crowdsourcing services, a set of socialnetwork analytic services and a set of algorithms for querying networkdomains.

In embodiments the set of data collection and monitoring servicesmonitors an environment selected from among a municipal environment, acorporate environment, a securities trading environment, a real propertyenvironment, a commercial facility, a warehousing facility, atransportation environment, a manufacturing environment, a storageenvironment, a home, and a vehicle.

In embodiments the debt transaction is of a type selected from among anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

In embodiments the entities involved in the set of debt transactionsinclude a set of parties and a set of assets.

In embodiments the set of assets includes assets among municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

In embodiments the platform or system may further include a set ofsensors positioned on at least one of the assets, on a container for theasset and on a package for the asset, the set of sensors configured toassociate sensor information sensed by the set of sensors with a uniqueidentifier for the asset and a set of blockchain services for takinginformation from the data collection and monitoring services and the setof sensors and storing the information in a blockchain, wherein accessto the blockchain is provided via a secure access control interface fora party for a debt transaction involving the asset.

In embodiments the set of sensors is selected from the group consistingof image, temperature, pressure, humidity, velocity, acceleration,rotational, torque, weight, chemical, magnetic field, electrical field,and position sensors.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of the assets and undertakes an actionrelated to a debt transaction to which the asset is related.

In embodiments the action is selected from among offering a debttransaction, underwriting a debt transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating debt, andconsolidating debt.

In embodiments the artificial intelligence services include at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the automated debt management system is trained on a setof interactions of parties with a set of user interfaces involved in aset of debt transaction activities.

In embodiments the set of debt transaction activities includesactivities among offering a debt transaction, underwriting a debttransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating title, managing inspection,recording a change in title, assessing the value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating debt, and consolidating debt.

In embodiments the platform or system may further include a market valuedata collection service that monitors and reports on marketplaceinformation relevant to the value of a set of assets.

In embodiments the set of assets includes assets among municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

In embodiments the market value data collection service monitors pricingor financial data for items that are similar to the assets in at leastone public marketplace.

In embodiments a set of similar items for valuing the assets isconstructed using a similarity clustering algorithm based on theattributes of the assets.

In embodiments the attributes are selected from among a category of theassets, asset age, asset condition, asset history, asset storage, andgeolocation of assets.

In embodiments the platform or system may further include a set of smartcontract services for managing a smart contract for the debttransaction.

In embodiments the smart contract services set terms and conditions forthe transaction.

In embodiments the set of terms and conditions for the debt transactionthat are specified and managed by the set of smart contract services isselected from among a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a specification ofcollateral, a specification of substitutability of collateral, a party,a guarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default.

Referring to FIG. 16, in embodiments a lending platform is providedhaving a system that varies the terms and conditions of loan based on aparameter monitored by the IoT. The loan may be a subsidized loan. TheRPA system 154 may provide automation for one or more aspects of a loanmanagement solution 248 that enables automated loan management and/orprovides a recommendation or plan for a loan management activityrelevant to a loan transaction, such as for personal loans, corporateloans, subsidized loans, student loans, or other loans, including onesthat may be backed by assets, collateral, or commitments of a borrower.The loan management solution 248 and/or RPA system 154 for loanmanagement may include a set of interfaces, workflows, and models (whichmay include, use or be enabled by various adaptive intelligent systems158) and other components that are configured to enable automation ofone or more aspects of a loan management action or a management processfor a loan transaction, such as based on a set of conditions, which mayinclude smart contract terms and conditions, marketplace conditions (ofplatform marketplaces and/or external marketplaces 188, conditionsmonitored by monitoring systems 164 and data collection systems 166, andthe like (such as of entities 198, including without limitation parties210, collateral 102 and assets 218, among others, as well as of interestrates, available lenders, available terms and the like). For example, auser of the loan management solution 248 may create, configure (such asusing one or more templates or libraries), modify, set or otherwisehandle (such as in a user interface of the loan management solution 248and/or RPA system 154) various rules, thresholds, conditionalprocedures, workflows, model parameters, and the like that determine, orrecommend, a loan management action or plan for management a set ofloans of a given type or types based on one or more events, conditions,states, actions, or the like, where the loan management plan may bebased on various factors, such as the interest rates available fromvarious primary and secondary lenders or issuers, permitted attributesof borrowers (e.g., based on income, wealth, location, or the like)prevailing interest rates in a platform marketplace or externalmarketplace, the status of the parties of a set of loans, the status orother attributes of collateral 102 or assets 218, risk factors of theborrower, one or more guarantors, market risk factors and the like(including predicted risk based on one or more predictive models usingartificial intelligence 156), status of debt, condition of collateral102 or assets 218 available to secure or back a set of loans, the stateof a business or business operation (e.g., receivables, payables, or thelike), conditions of parties 210 (such as net worth, wealth, debt,location, and other conditions), behaviors of parties (such as behaviorsindicating preferences, behaviors indicating debt preferences, paymentpreferences, or communication preferences), and many others. Loanmanagement may include management with respect to terms and conditionsof sets of loans, selection of appropriate loans, communications toencourage transactions, and the like. In embodiments the loan managementsolution 248 may automatically recommend or set rules, thresholds,actions, parameters and the like (optionally by learning to do so basedon a training set of outcomes over time), resulting in a recommendedloan management plan, which may specify a series of actions required toaccomplish a recommended or desired outcome of loan management (such aswithin a range of acceptable outcomes), which may be automated and mayinvolve conditional execution of steps based on monitored conditionsand/or smart contract terms, which may be created, configured, and/oraccounted for by the loan management plan. Loan management plans may bedetermined and executed based at least one part on market factors (suchas competing interest rates offered by other issuers, property values,attributes of issuers, values of collateral or assets, and the like) aswell as regulatory and/or compliance factors. Loan management plans maybe generated and/or executed for creation of new loans, for secondaryloans or transactions to back loans, for collection, for consolidation,for foreclosure, for situations of bankruptcy of insolvency, formodifications of existing loans, for situations involving market changes(e.g., changes in prevailing interest rates or property values) andothers. In embodiments, adaptive intelligent systems 158, includingartificial intelligence 156 may be trained on a training set of loanmanagement activities by experts and/or on outcomes of loan managementactions to generate a set of predictions, classifications, controlinstructions, plans, models, or the like for automated creation,management and/or execution of one or more aspects of a loan managementplan.

In embodiments, provided herein is a system for automating handling of asubsidized loan. In embodiments, the platform or system includes (a) aset of Internet of Things data collection and monitoring services forcollecting information about a set of entities involved in a set ofsubsidized loan transactions; (b) a condition classifying system havinga model and a set of artificial intelligence services for classifying aset of parameters of the set of subsidized loans involved in thetransactions based on information from the set of IoT data collectionservices 208, wherein the model is trained using a training data set ofoutcomes related to subsidized loans; and (c) a set of smart contractfor automatically modifying the terms and conditions of a subsidizedloan based on the classified set of parameters from the conditionclassifying system.

In embodiments the set of entities includes entities among a set ofsubsidized loans, a set of parties, a set of subsidies, a set ofguarantors, a set of subsidizing parties, and a set of collateral.

In embodiments a set of subsidizing parties includes at least one of amunicipality, a corporation, a contractor, a government entity, anon-governmental entity, and a non-profit entity.

In embodiments the set of subsidized loans includes at least one of amunicipal subsidized loan, a government subsidized loan, a student loan,an asset-backed subsidized loan, and a corporate subsidized loan.

In embodiments the condition classified by the condition classifyingsystem is among a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a contractual performancecondition, a policy risk condition, a financial health condition, aphysical defect condition, a physical health condition, an entity riskcondition and an entity health condition.

In embodiments the loan is a student loan and the condition classifyingsystem classifies at least one of the progress of a student toward adegree, the participation of a student in a non-profit activity, and theparticipation of the student in a public interest activity.

In embodiments the set of Internet of Things data collection andmonitoring services enables a user interface by which a user mayconfigure a query for information about the set of entities.

In embodiments the platform or system may further include a set ofconfigurable data collection and monitoring services for monitoring theentities that includes at least one of a set of social network analyticservices, a set of environmental condition sensors, a set ofcrowdsourcing services, and a set of algorithms for querying networkdomains.

In embodiments the set of configurable data collection and monitoringservices monitors an environment selected from among a municipalenvironment, an educational environment, a corporate environment, asecurities trading environment, a real property environment, acommercial facility, a warehousing facility, a transportationenvironment, a manufacturing environment, a storage environment, a home,and a vehicle.

In embodiments the set of subsidized loans is backed by a set of assets.

In embodiments the set of assets includes assets among municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of the assets and undertakes an actionrelated to a subsidized loan transaction to which the asset is related.

In embodiments the action is selected from among offering a subsidizedloan transaction, underwriting a subsidized loan transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating title, managing inspection, recording a change intitle, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating subsidized loans, and consolidating subsidized loans.

In embodiments the artificial intelligence services include at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the platform or system may further include an automatedsubsidized loan management system that manages an action related to thesubsidized loan, wherein the automated subsidized loan management systemis trained on a training set of subsidized loan management activities.

In embodiments the automated subsidized loan management system istrained on a set of interactions of parties with a set of userinterfaces involved in a set of subsidized loan transaction activities.

In embodiments the set of subsidized loan transaction activitiesincludes activities among offering a subsidized loan transaction,underwriting a subsidized loan transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating subsidizedloans, and consolidating subsidized loans.

In embodiments the platform or system may further include a set ofblockchain services for recording the modified set of terms andconditions for the set of subsidized loans in a distributed ledger.

In embodiments the platform or system may further include a market valuedata collection service that monitors and reports on marketplaceinformation relevant to the value of at least one of the issuer, a setof subsidized loans, and a set of assets.

In embodiments reporting is on a set of assets that includes at leastone of a municipal asset, a vehicle, a ship, a plane, a building, ahome, real estate property, undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

In embodiments the market value data collection service monitors pricingor financial data for items that are similar to the assets in at leastone public marketplace.

In embodiments a set of similar items for valuing the assets isconstructed using a similarity clustering algorithm based on theattributes of the assets.

In embodiments the attributes are selected from among a category of theassets, asset age, asset condition, asset history, asset storage, andgeolocation of assets.

In embodiments the platform or system may further include a set of smartcontract services for managing a smart contract for the subsidized loantransaction.

In embodiments the smart contract services set terms and conditions forthe subsidized loan.

In embodiments the set of terms and conditions for the debt transactionthat are specified and managed by the set of smart contract services isselected from among a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a specification of assetsthat back the subsidized loan, a specification of substitutability ofassets, a party, an issuer, a purchaser, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, and a consequence of default.

In embodiments a lending platform is provided having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored in a social network.

In embodiments, provided herein is a system for automating handling of asubsidized loan. In embodiments, the platform or system includes (a) aset of social network analytic data collection and monitoring servicesfor collecting information about a set of entities involved in a set ofsubsidized loan transactions; (b) a condition classifying system havinga model and a set of artificial intelligence services for classifying aset of parameters of the set of subsidized loans involved in thetransactions based on information from the set of social networkanalytics applications 204 which include data collection, monitoring,and analysis, wherein the model is trained using a training data set ofoutcomes related to subsidized loans; and (c) a set of smart contractfor automatically modifying the terms and conditions of a subsidizedloan based on the classified set of parameters from the conditionclassifying system.

In embodiments the set of entities includes entities among a set ofsubsidized loans, a set of parties, a set of subsidies, a set ofguarantors, a set of subsidizing parties, and a set of collateral.

In embodiments a set of subsidizing parties includes at least one of amunicipality, a corporation, a contractor, a government entity, anon-governmental entity, and a non-profit entity.

In embodiments the set of subsidized loans includes at least one of amunicipal subsidized loan, a government subsidized loan, a student loan,an asset-backed subsidized loan, and a corporate subsidized loan.

In embodiments the condition classified by the condition classifyingsystem is among a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a contractual performancecondition, a policy risk condition, a financial health condition, aphysical defect condition, a physical health condition, an entity riskcondition and an entity health condition.

In embodiments the loan is a student loan and the condition classifyingsystem classifies at least one of the progress of a student toward adegree, the participation of a student in a non-profit activity, and theparticipation of the student in a public interest activity.

In embodiments the set of social network analytic data collection andmonitoring services enables a user interface by which a user mayconfigure a query for information about the set of entities and thesocial network analytic data collection and monitoring servicesinitiates a set of algorithms that search and retrieve data from socialnetworks based on the query.

In embodiments the platform or system may further include a set ofconfigurable data collection and monitoring services for monitoring theentities that includes at least one of a set of Internet of Thingsservices, a set of environmental condition sensors, a set ofcrowdsourcing services, and a set of algorithms for querying networkdomains.

In embodiments the set of configurable data collection and monitoringservices monitors an environment selected from among a municipalenvironment, an educational environment, a corporate environment, asecurities trading environment, a real property environment, acommercial facility, a warehousing facility, a transportationenvironment, a manufacturing environment, a storage environment, a home,and a vehicle.

In embodiments the set of subsidized loans is backed by a set of assets.

In embodiments the set of assets includes assets among municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of the assets and undertakes an actionrelated to a subsidized loan transaction to which the asset is related.

In embodiments the action is selected from among offering a subsidizedloan transaction, underwriting a subsidized loan transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating title, managing inspection, recording a change intitle, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating subsidized loans, and consolidating subsidized loans.

In embodiments the artificial intelligence services include at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the platform or system may further include an automatedsubsidized loan management system that manages an action related to thesubsidized loan, wherein the automated subsidized loan management systemis trained on a training set of subsidized loan management activities.

In embodiments the automated subsidized loan management system istrained on a set of interactions of parties with a set of userinterfaces involved in a set of subsidized loan transaction activities.

In embodiments the set of subsidized loan transaction activitiesincludes activities among offering a subsidized loan transaction,underwriting a subsidized loan transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating subsidizedloans, and consolidating subsidized loans.

In embodiments the platform or system may further include a set ofblockchain services for recording the modified set of terms andconditions for the set of subsidized loans in a distributed ledger.

In embodiments the platform or system may further include a market valuedata collection service that monitors and reports on marketplaceinformation relevant to the value of at least one of a party, a set ofsubsidized loans, and a set of assets.

In embodiments reporting is on a set of assets that includes at leastone of a municipal asset, a vehicle, a ship, a plane, a building, ahome, real estate property, undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

In embodiments the market value data collection service monitors pricingor financial data for items that are similar to the assets in at leastone public marketplace.

In embodiments a set of similar items for valuing the assets isconstructed using a similarity clustering algorithm based on theattributes of the assets.

In embodiments the attributes are selected from among a category of theassets, asset age, asset condition, asset history, asset storage, andgeolocation of assets.

In embodiments the platform or system may further include a set of smartcontract services for managing a smart contract for the subsidized loantransaction.

In embodiments the smart contract services set terms and conditions forthe subsidized loan.

In embodiments the set of terms and conditions for the debt transactionthat are specified and managed by the set of smart contract services isselected from among a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a specification of assetsthat back the subsidized loan, a specification of substitutability ofassets, a party, an issuer, a purchaser, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, and a consequence of default.

In embodiments a lending platform is provided having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing.

In embodiments, provided herein is a system for automating handling of asubsidized loan. In embodiments, the platform or system includes (a) aset of crowdsourcing systems 520 for collecting information about a setof entities involved in a set of subsidized loan transactions; (b) acondition classifying system having a model and a set of artificialintelligence services for classifying a set of parameters of the set ofsubsidized loans involved in the transactions based on information fromthe set of crowdsourcing services, wherein the model is trained using atraining data set of outcomes related to subsidized loans; and (c) a setof smart contract for automatically modifying the terms and conditionsof a subsidized loan based on the classified set of parameters from thecondition classifying system.

In embodiments the set of entities includes entities among a set ofsubsidized loans, a set of parties, a set of subsidies, a set ofguarantors, a set of subsidizing parties, and a set of collateral.

In embodiments a set of subsidizing parties includes at least one of amunicipality, a corporation, a contractor, a government entity, anon-governmental entity, and a non-profit entity.

In embodiments the set of subsidized loans includes at least one of amunicipal subsidized loan, a government subsidized loan, a student loan,an asset-backed subsidized loan, and a corporate subsidized loan.

In embodiments the condition classified by the condition classifyingsystem is among a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a contractual performancecondition, a policy risk condition, a financial health condition, aphysical defect condition, a physical health condition, an entity riskcondition and an entity health condition.

In embodiments the loan is a student loan and the condition classifyingsystem classifies at least one of the progress of a student toward adegree, the participation of a student in a non-profit activity, and theparticipation of the student in a public interest activity.

In embodiments the set of crowdsourcing services enables a userinterface by which a user may configure a query for information aboutthe set of entities and the set of crowdsourcing services automaticallyconfigures initiates a crowdsourcing request based on the query.

In embodiments the platform or system may further include a set ofconfigurable data collection and monitoring services for monitoring theentities that includes at least one of a set of Internet of Thingsservices, a set of environmental condition sensors, a set of socialnetwork analytic services, and a set of algorithms for querying networkdomains.

In embodiments the set of configurable data collection and monitoringservices monitors an environment selected from among a municipalenvironment, an educational environment, a corporate environment, asecurities trading environment, a real property environment, acommercial facility, a warehousing facility, a transportationenvironment, a manufacturing environment, a storage environment, a home,and a vehicle.

In embodiments the set of subsidized loans is backed by a set of assets.

In embodiments the set of assets includes assets among municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

In embodiments the platform or system may further include an automatedagent that processes events relevant to at least one of the value, thecondition and the ownership of the assets and undertakes an actionrelated to a subsidized loan transaction to which the asset is related.

In embodiments the action is selected from among offering a subsidizedloan transaction, underwriting a subsidized loan transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating title, managing inspection, recording a change intitle, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating subsidized loans, and consolidating subsidized loans.

In embodiments the artificial intelligence services include at least oneof a machine learning system, a model-based system, a rule-based system,a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

In embodiments the platform or system may further include an automatedsubsidized loan management system that manages an action related to thesubsidized loan, wherein the automated subsidized loan management systemis trained on a training set of subsidized loan management activities.

In embodiments the automated subsidized loan management system istrained on a set of interactions of parties with a set of userinterfaces involved in a set of subsidized loan transaction activities.

In embodiments the set of subsidized loan transaction activitiesincludes activities among offering a subsidized loan transaction,underwriting a subsidized loan transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating subsidizedloans, and consolidating subsidized loans.

In embodiments the platform or system may further include a set ofblockchain services for recording the modified set of terms andconditions for the set of subsidized loans in a distributed ledger.

In embodiments the platform or system may further include a market valuedata collection service that monitors and reports on marketplaceinformation relevant to the value of at least one of a party, a set ofsubsidized loans, and a set of assets.

In embodiments reporting is on a set of assets that includes at leastone of a municipal asset, a vehicle, a ship, a plane, a building, ahome, real estate property, undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

In embodiments the market value data collection service monitors pricingor financial data for items that are similar to the assets in at leastone public marketplace.

In embodiments a set of similar items for valuing the assets isconstructed using a similarity clustering algorithm based on theattributes of the assets.

In embodiments the attributes are selected from among a category of theassets, asset age, asset condition, asset history, asset storage, andgeolocation of assets.

In embodiments the platform or system may further include a set of smartcontract services for managing a smart contract for the subsidized loantransaction.

In embodiments the smart contract services set terms and conditions forthe subsidized loan.

In embodiments the set of terms and conditions for the debt transactionthat are specified and managed by the set of smart contract services isselected from among a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a specification of assetsthat back the subsidized loan, a specification of substitutability ofassets, a party, an issuer, a purchaser, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, and a consequence of default.

Referring to FIG. 17, in embodiments a lending platform is providedhaving an automated blockchain custody service and solution for managinga set of custodial assets. The RPA system 154 may provide automation forone or more aspects of a custodial solution 1802 that enables automatedcustodial management and/or provides a recommendation or plan for acustodial activity relevant to a set of assets, such as ones involved inor backing a lending transaction or ones for which clients seekcustodial for security or administrative purposes, such as for assets ofany of the types described herein, including cryptocurrencies and othercurrencies, stock certificates and other evidence of ownership,securities, and many others. The custodial solution 1802 and/or RPAsystem 154 for handling custodial activity may include a set ofinterfaces, workflows, and models (which may include, use or be enabledby various adaptive intelligent systems 158) and other components thatare configured to enable automation of one or more aspects of acustodial action or a management process for trust or custody of a setof assets 218, such as based on a set of conditions, which may includesmart contract terms and conditions, marketplace conditions (of platformmarketplaces and/or external marketplaces 188, conditions monitored bymonitoring systems 164 and data collection systems 166, and the like(such as of entities 198, including without limitation parties 210,collateral 102 and assets 218, among others, and the like). For example,a user of the custodial solution 1802 may create, configure (such asusing one or more templates or libraries), modify, set or otherwisehandle (such as in a user interface of the custodial solution 1802and/or RPA system 154) various rules, thresholds, conditionalprocedures, workflows, model parameters, and the like that determine, orrecommend, a custodial action or plan for management a set of assets ofa given type or types based on one or more events, conditions, states,actions, status or the like, where the custodial plan may be based onvarious factors, such as the storage options available, the basis forretrieval of assets, the basis for transfer of ownership of assets, andthe like, condition of assets 218 for which custodial services will berequired, behaviors of parties (such as behaviors indicatingpreferences), and many others. Custodial services may include managementwith respect to terms and conditions of sets of assets, selection ofappropriate terms and conditions for trust and custody 150, selection ofparameters for transfer of ownership, selection and provision ofstorage, selection and provision of secure infrastructure for datastorage, and others. In embodiments the custodial solution 1802 mayautomatically recommend or set rules, thresholds, actions, parametersand the like (optionally by learning to do so based on a training set ofoutcomes over time), resulting in a recommended custodial plan, whichmay specify a series of actions required to accomplish a recommended ordesired outcome of custodial services (such as within a range ofacceptable outcomes), which may be automated and may involve conditionalexecution of steps based on monitored conditions and/or smart contractterms, which may be created, configured, and/or accounted for by thecustodial plan. Custodial plans may be determined and executed based atleast one part on market factors (such as competing terms and conditionsoffered by other custodians, property values, attributes of clients,values of collateral or assets, costs of physical storage, costs of datastorage, and the like) as well as regulatory and/or compliance factors.In embodiments, adaptive intelligent systems 158, including artificialintelligence 156 may be trained on a training set of custodialactivities by experts and/or on outcomes of custodial actions togenerate a set of predictions, classifications, control instructions,plans, models, or the like for automated creation, management and/orexecution of one or more aspects of a custodial plan. In embodiments,actions with respect to custody of a set of assets may be stored in ablockchain 136, such as in a distributed ledger.

In embodiments, provided herein is a system for handling trust andcustody 150 for a set of assets. The platform or system may include (a)a set of asset identification services for identifying a set of assetsfor which a financial institution is responsible for taking custody; (b)a set of identity management services by which the financial institutionverifies identities and credentials of a set of entities entitled totake action with respect to the assets; and (c) set of blockchainservices wherein at least one of the set of assets and identifyinginformation for the set of assets is stored in a blockchain and whereinevents related to the set of assets are recorded in a distributedledger.

In embodiments the credentials include owner credentials, agentcredentials, beneficiary credentials, trustee credentials, and custodiancredentials.

In embodiments the events related to the set of assets include transferof title, death of an owner, disability of an owner, bankruptcy of anowner, foreclosure, placement of a lien, use of assets as collateral,designation of a beneficiary, undertaking a loan against assets,providing a notice with respect to assets, inspection of assets,assessment of assets, reporting on assets for taxation purposes,allocation of ownership of assets, disposal of assets, sale of assets,purchase of assets, and designation of an ownership status.

In embodiments the platform or system further includes a set of datacollection and monitoring services for monitoring at least one of theset of assets, a set of entities, and a set of events related to theassets.

In embodiments the set of entities includes at least one of an owner, abeneficiary, an agent, a trustee and a custodian.

In embodiments the platform or system further includes a set of smartcontract services for managing the custody of the set of assets, whereinat least one event related to the set of assets is managed automaticallyby the smart contract based on a set of terms and conditions embodied inthe smart contract and based on information collected by the set of datacollection and monitoring services.

In embodiments the events related to the set of assets include transferof title, death of an owner, disability of an owner, bankruptcy of anowner, foreclosure, placement of a lien, use of assets as collateral,designation of a beneficiary, undertaking a loan against assets,providing a notice with respect to assets, inspection of assets,assessment of assets, reporting on assets for taxation purposes,allocation of ownership of assets, disposal of assets, sale of assets,purchase of assets, and designation of an ownership status.

Referring to FIG. 18, in embodiments a lending platform is providedhaving an underwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.The RPA system 154 may provide automation for one or more aspects of anunderwriting solution 103 that enables automated underwriting and/orprovides a recommendation or plan for an underwriting activity relevantto a loan transaction, such as for personal loans, corporate loans,subsidized loans, student loans, or other loans, including ones that maybe backed by assets, collateral, or commitments of a borrower. Theunderwriting solution 103 and/or RPA system 154 for underwriting mayinclude a set of interfaces, workflows, and models (which may include,use or be enabled by various adaptive intelligent systems 158) and othercomponents that are configured to enable automation of one or moreaspects of a underwriting action or a management process for a loantransaction, such as based on a set of conditions, which may includesmart contract terms and conditions, marketplace conditions (of platformmarketplaces and/or external marketplaces 188, conditions monitored bymonitoring systems 164 and data collection systems 166, and the like(such as of entities 198, including without limitation parties 210,collateral 102 and assets 218, among others, as well as of interestrates, available lenders, available terms and the like)). For example, auser of the underwriting solution 103 may create, configure (such asusing one or more templates or libraries), modify, set or otherwisehandle (such as in a user interface of the underwriting solution 103and/or RPA system 154) various rules, thresholds, conditionalprocedures, workflows, model parameters, and the like that determine, orrecommend, a underwriting action or plan for management a set of loansof a given type or types based on one or more events, conditions,states, actions, or the like, where the underwriting plan may be basedon various factors, such as the interest rates available from variousprimary and secondary lenders or issuers, permitted attributes ofborrowers (e.g., based on income, wealth, location, or the like),prevailing interest rates in a platform marketplace or externalmarketplace, the status of the parties of a set of loans, the status orother attributes of collateral 102 or assets 218, risk factors of theborrower, one or more guarantors, market risk factors and the like(including predicted risk based on one or more predictive models usingartificial intelligence 156), status of debt, condition of collateral102 or assets 218 available to secure or back a set of loans, the stateof a business or business operation (e.g., receivables, payables, or thelike), conditions of parties 210 (such as net worth, wealth, debt,location, and other conditions), behaviors of parties (such as behaviorsindicating preferences, behaviors indicating debt preferences, paymentpreferences, or communication preferences), and many others.Underwriting may include management with respect to terms and conditionsof sets of loans, selection of appropriate loans, communicationsrelevant to underwriting processes, and the like. In embodiments theunderwriting solution 103 may automatically recommend or set rules,thresholds, actions, parameters and the like (optionally by learning todo so based on a training set of outcomes over time), resulting in arecommended underwriting plan, which may specify a series of actionsrequired to accomplish a recommended or desired outcome of underwriting(such as within a range of acceptable outcomes), which may be automatedand may involve conditional execution of steps based on monitoredconditions and/or smart contract terms, which may be created,configured, and/or accounted for by the underwriting plan. Underwritingplans may be determined and executed based at least one part on marketfactors (such as competing interest rates offered by other issuers,property values, borrower behavior, demographic trends, payment trends,attributes of issuers, values of collateral or assets, and the like) aswell as regulatory and/or compliance factors. Underwriting plans may begenerated and/or executed for new loans, for secondary loans ortransactions to back loans, for collection, for consolidation, forforeclosure, for situations of bankruptcy of insolvency, formodifications of existing loans, for situations involving market changes(e.g., changes in prevailing interest rates or property values), forforeclosure activities, and others. In embodiments, adaptive intelligentsystems 158, including artificial intelligence 156 may be trained on atraining set of underwriting activities by experts and/or on outcomes ofunderwriting actions to generate a set of predictions, classifications,control instructions, plans, models, or the like for automated creation,management and/or execution of one or more aspects of an underwritingplan. In embodiments events and outcomes of underwriting may be recordedin a blockchain 136, such as in a distributed ledger, for secure accessand retrieval by authorized users. Adaptive intelligent systems 158 may,such as using various artificial intelligence 156 or expert systemsdisclosed herein and in the documented incorporated by reference herein,may improve or automated one or more aspects of underwriting, such as bytraining a model, a neural net, a deep learning system, or the likebased on a training set of expert interactions and/or a training set ofoutcomes from underwriting activities.

Referring to FIG. 19, in embodiments a lending platform is providedhaving a loan marketing system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services and smart contractservices for marketing a loan to a set of prospective parties. Thelending enablement platform 100 may enable one or more aspects of a loanmarketing solution 2002 that enables automated loan marketing and/orprovides a recommendation or plan for a loan marketing activity relevantto a loan transaction, such as for personal loans, corporate loans,subsidized loans, student loans, or other loans, including ones that maybe backed by assets, collateral, or commitments of a borrower. The loanmarketing solution 2002 (which in embodiments may include or use an RPAsystem 154 configured for loan marketing) may include a set ofinterfaces, workflows, and models (which may include, use or be enabledby various adaptive intelligent systems 158) and other components thatare configured to enable automation of one or more aspects of a loanmarketing action or a management process for a loan transaction, such asbased on a set of conditions, which may include smart contract terms andconditions (which may be configured, e.g., for a marketed set of loans),available capital for lending, regulatory factors, marketplaceconditions (of platform marketplaces and/or external marketplaces 188,conditions monitored by monitoring systems 164 and data collectionsystems 166, and the like (such as of entities 198, including withoutlimitation parties 210, collateral 102 and assets 218, among others, aswell as of interest rates, available lenders, available terms and thelike)), and others. For example, a user of the loan marketing solution2002 may create, configure (such as using one or more templates orlibraries), modify, set or otherwise handle (such as in a user interfaceof the loan marketing solution 2002 and/or RPA system 154) variousrules, thresholds, conditional procedures, workflows, model parameters,and the like that determine, or recommend, a loan marketing action orplan for management a set of loans of a given type or types based on oneor more events, conditions, states, actions, or the like, where the loanmarketing plan may be based on various factors, such as the interestrates available from various primary and secondary lenders or issuers,returns on the capital that is made available for loans, permitted ordesired attributes of borrowers (e.g., based on income, wealth,location, or the like), prevailing interest rates in a platformmarketplace or external marketplace, the status of the parties of a setof loans, the status or other attributes of collateral 102 or assets218, risk factors of the borrower, one or more guarantors, market riskfactors and the like (including predicted risk based on one or morepredictive models using artificial intelligence 156), status of debt,condition of collateral 102 or assets 218 available to secure or back aset of loans, the state of a business or business operation (e.g.,receivables, payables, or the like), conditions of parties 210 (such asnet worth, wealth, debt, location, and other conditions), behaviors ofparties (such as behaviors indicating preferences, behaviors indicatingdebt preferences, payment preferences, or communication preferences),and many others. Loan marketing may include management with respect toterms and conditions of sets of loans, selection of appropriate loans,communications relevant to loan marketing processes, and the like. Inembodiments the loan marketing solution 2002 may automatically recommendor set rules, thresholds, actions, parameters and the like (optionallyby learning to do so based on a training set of outcomes over time),resulting in a recommended loan marketing plan, which may specify aseries of actions required to accomplish a recommended or desiredoutcome of loan marketing (such as within a range of acceptableoutcomes), which may be automated and may involve conditional executionof steps based on monitored conditions and/or smart contract terms,which may be created, configured, and/or accounted for by the loanmarketing plan. Loan marketing plans may be determined and executedbased at least one part on market factors (such as competing interestrates offered by other issuers, property values, borrower behavior,demographic trends, payment trends, attributes of issuers, values ofcollateral or assets, and the like) as well as regulatory and/orcompliance factors. Loan marketing plans may be generated and/orexecuted for new loans, for secondary loans or transactions to backloans, for collection, for consolidation, for foreclosure situations(e.g., as an alternative to foreclosure), for situations of bankruptcyof insolvency, for modifications of existing loans, for situationsinvolving market changes (e.g., changes in prevailing interest rates,available capital, or property values), and others. In embodiments,adaptive intelligent systems 158, including artificial intelligence 156may be trained on a training set of loan marketing activities by expertsand/or on outcomes of loan marketing actions to generate a set ofpredictions, classifications, control instructions, plans, models, orthe like for automated creation, management and/or execution of one ormore aspects of a loan marketing plan. In embodiments events andoutcomes of loan marketing may be recorded in a blockchain 136, such asin a distributed ledger, for secure access and retrieval by authorizedusers. Adaptive intelligent systems 158 may, such as using variousartificial intelligence 156 or expert systems disclosed herein and inthe documented incorporated by reference herein, may improve orautomated one or more aspects of entity rating, such as by training amodel, a neural net, a deep learning system, or the like based on atraining set of expert interactions and/or a training set of outcomesfrom loan marketing activities.

Referring to FIG. 20, in embodiments a lending platform is providedhaving a rating system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services, and smart contract services for ratinga set of loan-related entities. The lending enablement platform 100 mayenable one or more aspects of an entity rating solution 206 that enablesautomated entity rating and/or provides a recommendation or plan for anentity rating activity relevant to a loan transaction, such as forpersonal loans, corporate loans, subsidized loans, student loans, orother loans, including ones that may be backed by assets, collateral, orcommitments of a borrower. The entity rating solution 206 (which inembodiments may include or use an RPA system 154 configured for entityrating) may include a set of interfaces, workflows, and models (whichmay include, use or be enabled by various adaptive intelligent systems158) and other components that are configured to enable automation ofone or more aspects of an entity rating action or a rating process for aloan transaction, such as based on a set of conditions, attributes,events, or the like, which may include attributes of entities 198 (suchas value, quality, location, net worth, price, physical condition,health condition, security, safety, ownership and the like), smartcontract terms and conditions (which may be configured or populated,e.g., based on ratings for a rated set of loans), regulatory factors,marketplace conditions (of platform marketplaces and/or externalmarketplaces 188, conditions monitored by monitoring systems 164 anddata collection systems 166, and the like (such as of entities 198,including without limitation parties 210, collateral 102 and assets 218,among others, as well as of interest rates, available lenders, availableterms and the like)), and others. For example, a user of the entityrating solution 206 may create, configure (such as using one or moretemplates or libraries), modify, set or otherwise handle (such as in auser interface of the entity rating solution 206 and/or RPA system 154)various rules, thresholds, conditional procedures, workflows, modelparameters, and the like that determine, or recommend, an entity ratingaction or plan for rating a set of loans of a given type or types basedon one or more events, attributes, parameters, characteristics,conditions, states, actions, or the like, where the entity rating planmay be based on various factors (e.g., based on income, wealth,location, or the like or parties 210, relative to others, or based oncondition of collateral 102 or assets 218, or the like), prevailingconditions of a platform marketplace or external marketplace, the statusof the parties of a set of loans, the status or other attributes ofcollateral 102 or assets 218, risk factors of the borrower, one or moreguarantors, market risk factors and the like (including predicted riskbased on one or more predictive models using artificial intelligence156), status of debt, condition of collateral 102 or assets 218available to secure or back a set of loans, the state of a business orbusiness operation (e.g., receivables, payables, or the like),conditions of parties 210 (such as net worth, wealth, debt, location,and other conditions), behaviors of parties (such as behaviorsindicating preferences, behaviors indicating debt preferences, paymentpreferences, or communication preferences), and many others. Entityrating may include management with respect to terms and conditions ofsets of loans, selection of appropriate loans, communications relevantto entity rating processes, and the like. In embodiments the entityrating solution 206 may automatically recommend or set rules,thresholds, actions, parameters and the like (optionally by learning todo so based on a training set of outcomes over time), resulting in arecommended entity rating plan, which may specify a series of actionsrequired to accomplish a recommended or desired outcome of entity rating(such as within a range of acceptable outcomes), which may be automatedand may involve conditional execution of steps based on monitoredconditions and/or smart contract terms, which may be created,configured, and/or accounted for by the entity rating plan. Entityrating plans may be determined and executed based at least one part onmarket factors (such as competing interest rates offered by otherissuers, property values, borrower behavior, demographic trends, paymenttrends, attributes of issuers, values of collateral or assets, and thelike) as well as regulatory and/or compliance factors. Entity ratingplans may be generated and/or executed for new loans, for secondaryloans or transactions to back loans, for collection, for consolidation,for foreclosure situations (e.g., as an alternative to foreclosure), forsituations of bankruptcy of insolvency, for modifications of existingloans, for situations involving market changes (e.g., changes inprevailing interest rates, available capital, or property values), andothers. In embodiments, adaptive intelligent systems 158, includingartificial intelligence 156 may be trained on a training set of entityrating activities by experts and/or on outcomes of entity rating actionsto generate a set of predictions, classifications, control instructions,plans, models, or the like for automated creation, management and/orexecution of one or more aspects of an entity rating plan. Inembodiments events and outcomes of entity rating may be recorded in ablockchain 136, such as in a distributed ledger, for secure access andretrieval by authorized users. Adaptive intelligent systems 158 may,such as using various artificial intelligence 156 or expert systemsdisclosed herein and in the documented incorporated by reference herein,may improve or automated one or more aspects of entity rating, such asby training a model, a neural net, a deep learning system, or the likebased on a training set of expert interactions and/or a training set ofoutcomes from entity rating activities.

Referring to FIG. 21, in embodiments a lending platform is providedhaving a regulatory and/or compliance solution 142 with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation and a policy that applies to alending transaction. The lending enablement platform 100 may enable oneor more aspects of a regulatory and compliance solution 142 that enablesautomated regulatory and compliance and/or provides a recommendation orplan for a regulatory and compliance activity relevant to a loantransaction, such as for personal loans, corporate loans, subsidizedloans, student loans, or other loans, including ones that may be backedby assets, collateral, or commitments of a borrower. The regulatory andcompliance solution 142 (which in embodiments may include or use an RPAsystem 154 configured for automating regulatory and complianceactivities based on a training set of interactions by experts inregulatory and/or compliance activities) may include a set ofinterfaces, workflows, and models (which may include, use or be enabledby various adaptive intelligent systems 158) and other components thatare configured to enable automation of one or more aspects of aregulatory and compliance action or a regulatory and/or complianceprocess for a loan transaction, such as based on a set of policies,regulations, laws, requirements, specifications, conditions, attributes,events, or the like, which may include attributes of or applicable toentities 198 involved in a lending transaction and/or the terms andconditions of loans (including smart contract terms and conditions(which may be configured or populated, e.g., based on terms andconditions that are permitted for a given set of loans)), as well asvarious marketplace conditions (of platform marketplaces and/or externalmarketplaces 188, conditions monitored by monitoring systems 164 anddata collection systems 166, and the like (such as of entities 198,including without limitation parties 210, collateral 102 and assets LPX218, among others, as well as of interest rates, available lenders,available terms and the like)), and others. For example, a user of theregulatory and compliance solution 142 may create, configure (such asusing one or more templates or libraries), modify, set or otherwisehandle (such as in a user interface of the regulatory and/or compliancesolution 142 and/or RPA system 154) various rules, thresholds,conditional procedures, workflows, model parameters, and the like thatdetermine, or recommend, a regulatory and compliance action or plan forgoverning a set of loans of a given type or types based on one or moreevents, attributes, parameters, characteristics, conditions, states,actions, or the like, where the regulatory and compliance plan may bebased on various factors (e.g., based on permitted interest rates,required notices (e.g., regarding annualized percentage rate reporting),permitted borrowers (e.g., students for federally subsidized studentloans), permitted lenders, permitted issuers, income (e.g., forlow-income loans), wealth (e.g., for loans that are permitted by policyto be provided only to adequately capitalized parties), location (e.g.,for geographically governed lending programs, such as for municipaldevelopment), conditions of a platform marketplace or externalmarketplace (such as where loans are required to have interest ratesthat do not exceed a threshold that is calculated based on prevailinginterest rates), the status of the parties of a set of loans, the statusor other attributes of collateral 102 or assets 218, risk factors of theborrower, one or more guarantors, market risk factors and the like(including predicted risk based on one or more predictive models usingartificial intelligence 156), status of debt, condition of collateral102 or assets 218 available to secure or back a set of loans, the stateof a business or business operation (e.g., receivables, payables, or thelike), conditions of parties 210 (such as net worth, wealth, debt,location, and other conditions), behaviors of parties (such as behaviorsindicating preferences, behaviors indicating debt preferences, paymentpreferences, or communication preferences), and many others. Regulatoryand compliance may include governance with respect to terms andconditions of sets of loans, selection of appropriate loans, noticesrequired to be provided, underwriting policies, communications relevantto regulatory and compliance processes, and the like. In embodiments theregulatory and compliance solution 142 may automatically recommend orset rules, thresholds, actions, parameters and the like (optionally bylearning to do so based on a training set of outcomes over time),resulting in a recommended regulatory and compliance plan, which mayspecify a series of actions required to accomplish a recommended ordesired outcome of regulatory and compliance (such as within a range ofacceptable outcomes), which may be automated and may involve conditionalexecution of steps based on monitored conditions and/or smart contractterms, which may be created, configured, and/or accounted for by theregulatory and compliance plan. Regulatory and compliance plans may bedetermined and executed based at least one part on market factors (suchas competing interest rates offered by other issuers, property values,borrower behavior, demographic trends, payment trends, attributes ofissuers, values of collateral or assets, and the like) as well asregulatory and/or compliance factors. Regulatory and compliance plansmay be generated and/or executed for new loans, for secondary loans ortransactions to back loans, for collection, for consolidation, forforeclosure situations (e.g., as an alternative to foreclosure), forsituations of bankruptcy of insolvency, for modifications of existingloans, for situations involving market changes (e.g., changes inprevailing interest rates, available capital, or property values), andothers. In embodiments, adaptive intelligent systems 158, includingartificial intelligence 156 may be trained on a training set ofregulatory and compliance activities by experts and/or on outcomes ofregulatory and compliance actions to generate a set of predictions,classifications, control instructions, plans, models, or the like forautomated creation, management and/or execution of one or more aspectsof a regulatory and compliance plan. In embodiments events and outcomesof regulatory and compliance may be recorded in a blockchain 136, suchas in a distributed ledger, for secure access and retrieval byauthorized users. Adaptive intelligent systems 158 may, such as usingvarious artificial intelligence 156 or expert systems disclosed hereinand in the documented incorporated by reference herein, may improve orautomate one or more aspects of regulatory and compliance, such as bytraining a model, a neural net, a deep learning system, or the likebased on a training set of expert interactions and/or a training set ofoutcomes from regulatory and compliance activities.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having an Internet of Things andsensor platform for monitoring at least one of a set of assets and a setof collateral for a loan, a bond, or a debt transaction.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a smart contract anddistributed ledger platform for managing at least one of ownership of aset of collateral and a set of events related to a set of collateral.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a smart contract systemthat automatically adjusts an interest rate for a loan based oninformation collected via at least one of an Internet of Things system,a crowdsourcing system, a set of social network analytic services and aset of data collection and monitoring services.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a crowdsourcing system forobtaining information about at least one of a state of a set ofcollateral for a loan and a state of an entity relevant to a guaranteefor a loan.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a smart contract thatautomatically adjusts an interest rate for a loan based on at least oneof a regulatory factor and a market factor for a specific jurisdiction.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a smart contract thatautomatically restructures debt based on a monitored condition.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a social network monitoringsystem for validating the reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having an Internet of Things datacollection and monitoring system for validating reliability of aguarantee for a loan.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a robotic processautomation system for negotiation of a set of terms and conditions for aloan.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a robotic processautomation system for loan collection.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a robotic processautomation system for consolidating a set of loans.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a robotic processautomation system for managing a factoring loan.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a robotic processautomation system for brokering a mortgage loan.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a crowdsourcing andautomated classification system for validating condition of an issuerfor a bond.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a social network monitoringsystem with artificial intelligence for classifying a condition about abond.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having an Internet of Things datacollection and monitoring system with artificial intelligence forclassifying a condition about a bond.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a system that varies theterms and conditions of a subsidized loan based on a parameter monitoredby the IoT.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a system that varies theterms and conditions of a subsidized loan based on a parameter monitoredin a social network.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a system that varies theterms and conditions of a subsidized loan based on a parameter monitoredby crowdsourcing.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having an automated blockchaincustody service for managing a set of custodial assets.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having an underwriting system fora loan with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a loan marketing systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices and smart contract services for marketing a loan to a set ofprospective parties.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a rating system with a setof data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities.

In embodiments a lending platform is provided herein having a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, and smart contract services for handlinglending entities and transactions and having a compliance system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation and a policy relatedto a lending transaction.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a smart contract and distributed ledger platform for managingat least one of ownership of a set of collateral and a set of eventsrelated to a set of collateral.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a smart contract system that automatically adjusts aninterest rate for a loan based on information collected via at least oneof an Internet of Things system, a crowdsourcing system, a set of socialnetwork analytic services and a set of data collection and monitoringservices.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a crowdsourcing system for obtaining information about atleast one of a state of a set of collateral for a loan and a state of anentity relevant to a guarantee for a loan.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a smart contract that automatically adjusts an interest ratefor a loan based on at least one of a regulatory factor and a marketfactor for a specific jurisdiction.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a smart contract that automatically restructures debt basedon a monitored condition.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a social network monitoring system for validating thereliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having an Internet of Things data collection and monitoring systemfor validating reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a robotic process automation system for negotiation of a setof terms and conditions for a loan.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a robotic process automation system for loan collection.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a robotic process automation system for consolidating a setof loans.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a robotic process automation system for managing a factoringloan.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a robotic process automation system for brokering a mortgageloan.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a crowdsourcing and automated classification system forvalidating condition of an issuer for a bond.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a social network monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having an Internet of Things data collection and monitoring systemwith artificial intelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored in a social network.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a system that varies the terms and conditions of a subsidizedloan based on a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having an automated blockchain custody service for managing a set ofcustodial assets.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a loan marketing system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services and smart contractservices for marketing a loan to a set of prospective parties.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a rating system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services, and smart contract services for ratinga set of loan-related entities.

In embodiments a lending platform is provided herein having an Internetof Things and sensor platform for monitoring at least one of a set ofassets and a set of collateral for a loan, a bond, or a debt transactionand having a compliance system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for automatically facilitating compliance with atleast one of a law, a regulation and a policy related to a lendingtransaction.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a smart contract system that automatically adjustsan interest rate for a loan based on information collected via at leastone of an Internet of Things system, a crowdsourcing system, a set ofsocial network analytic services and a set of data collection andmonitoring services.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a crowdsourcing system for obtaining informationabout at least one of a state of a set of collateral for a loan and astate of an entity relevant to a guarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a smart contract that automatically adjusts aninterest rate for a loan based on at least one of a regulatory factorand a market factor for a specific jurisdiction.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a smart contract that automatically restructuresdebt based on a monitored condition.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a social network monitoring system for validatingthe reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having an Internet of Things data collection andmonitoring system for validating reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a robotic process automation system fornegotiation of a set of terms and conditions for a loan.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a robotic process automation system for loancollection.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a robotic process automation system forconsolidating a set of loans.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a robotic process automation system for managing afactoring loan.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a robotic process automation system for brokeringa mortgage loan.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a crowdsourcing and automated classificationsystem for validating condition of an issuer for a bond.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a social network monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having an Internet of Things data collection andmonitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a system that varies the terms and conditions of asubsidized loan based on a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a system that varies the terms and conditions of asubsidized loan based on a parameter monitored in a social network.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a system that varies the terms and conditions of asubsidized loan based on a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having an automated blockchain custody service formanaging a set of custodial assets.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a loan marketing system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services andsmart contract services for marketing a loan to a set of prospectiveparties.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a rating system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for rating a set of loan-related entities.

In embodiments a lending platform is provided herein having a smartcontract and distributed ledger platform for managing at least one ofownership of a set of collateral and a set of events related to a set ofcollateral and having a compliance system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for automatically facilitating compliance with atleast one of a law, a regulation and a policy related to a lendingtransaction.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga crowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga smart contract that automatically adjusts an interest rate for a loanbased on at least one of a regulatory factor and a market factor for aspecific jurisdiction.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga smart contract that automatically restructures debt based on amonitored condition.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga social network monitoring system for validating the reliability of aguarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havingan Internet of Things data collection and monitoring system forvalidating reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga robotic process automation system for negotiation of a set of termsand conditions for a loan.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga robotic process automation system for loan collection.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga robotic process automation system for consolidating a set of loans.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga robotic process automation system for managing a factoring loan.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga robotic process automation system for brokering a mortgage loan.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga crowdsourcing and automated classification system for validatingcondition of an issuer for a bond.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga social network monitoring system with artificial intelligence forclassifying a condition about a bond.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havingan Internet of Things data collection and monitoring system withartificial intelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga system that varies the terms and conditions of a subsidized loan basedon a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga system that varies the terms and conditions of a subsidized loan basedon a parameter monitored in a social network.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga system that varies the terms and conditions of a subsidized loan basedon a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havingan automated blockchain custody service for managing a set of custodialassets.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havingan underwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga loan marketing system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services and smart contract services formarketing a loan to a set of prospective parties.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga rating system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities.

In embodiments a lending platform is provided herein having a smartcontract system that automatically adjusts an interest rate for a loanbased on information collected via at least one of an Internet of Thingssystem, a crowdsourcing system, a set of social network analyticservices and a set of data collection and monitoring services and havinga compliance system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services, and smart contract services forautomatically facilitating compliance with at least one of a law, aregulation and a policy related to a lending transaction.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a smart contract thatautomatically adjusts an interest rate for a loan based on at least oneof a regulatory factor and a market factor for a specific jurisdiction.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a smart contract thatautomatically restructures debt based on a monitored condition.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a social networkmonitoring system for validating the reliability of a guarantee for aloan.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having an Internet of Things datacollection and monitoring system for validating reliability of aguarantee for a loan.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a robotic processautomation system for negotiation of a set of terms and conditions for aloan.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a robotic processautomation system for loan collection.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a robotic processautomation system for consolidating a set of loans.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a robotic processautomation system for managing a factoring loan.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a robotic processautomation system for brokering a mortgage loan.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a crowdsourcing andautomated classification system for validating condition of an issuerfor a bond.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a social networkmonitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having an Internet of Things datacollection and monitoring system with artificial intelligence forclassifying a condition about a bond.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a system that varies theterms and conditions of a subsidized loan based on a parameter monitoredby the IoT.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a system that varies theterms and conditions of a subsidized loan based on a parameter monitoredin a social network.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a system that varies theterms and conditions of a subsidized loan based on a parameter monitoredby crowdsourcing.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having an automated blockchaincustody service for managing a set of custodial assets.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having an underwriting system fora loan with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a loan marketing systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices and smart contract services for marketing a loan to a set ofprospective parties.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a rating system with a setof data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities.

In embodiments a lending platform is provided herein having acrowdsourcing system for obtaining information about at least one of astate of a set of collateral for a loan and a state of an entityrelevant to a guarantee for a loan and having a compliance system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation and a policy relatedto a lending transaction.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a smart contract that automatically restructuresdebt based on a monitored condition.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a social network monitoring system forvalidating the reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having an Internet of Things data collection andmonitoring system for validating reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a robotic process automation system fornegotiation of a set of terms and conditions for a loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a robotic process automation system for loancollection.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a robotic process automation system forconsolidating a set of loans.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a robotic process automation system for managinga factoring loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a robotic process automation system forbrokering a mortgage loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a crowdsourcing and automated classificationsystem for validating condition of an issuer for a bond.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a social network monitoring system withartificial intelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having an Internet of Things data collection andmonitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a system that varies the terms and conditions ofa subsidized loan based on a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a system that varies the terms and conditions ofa subsidized loan based on a parameter monitored in a social network.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a system that varies the terms and conditions ofa subsidized loan based on a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having an automated blockchain custody service formanaging a set of custodial assets.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a loan marketing system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services andsmart contract services for marketing a loan to a set of prospectiveparties.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a rating system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for rating a set of loan-related entities.

In embodiments a lending platform is provided herein having a smartcontract that automatically adjusts an interest rate for a loan based onat least one of a regulatory factor and a market factor for a specificjurisdiction and having a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation and a policy related to a lendingtransaction.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a social network monitoring system for validatingthe reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having an Internet of Things data collection andmonitoring system for validating reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a robotic process automation system for negotiationof a set of terms and conditions for a loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a robotic process automation system for loancollection.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a robotic process automation system forconsolidating a set of loans.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a robotic process automation system for managing afactoring loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a robotic process automation system for brokering amortgage loan.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a crowdsourcing and automated classification systemfor validating condition of an issuer for a bond.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a social network monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having an Internet of Things data collection andmonitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a system that varies the terms and conditions of asubsidized loan based on a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a system that varies the terms and conditions of asubsidized loan based on a parameter monitored in a social network.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a system that varies the terms and conditions of asubsidized loan based on a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having an automated blockchain custody service formanaging a set of custodial assets.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a loan marketing system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services andsmart contract services for marketing a loan to a set of prospectiveparties.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a rating system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for rating a set of loan-related entities.

In embodiments a lending platform is provided herein having a smartcontract that automatically restructures debt based on a monitoredcondition and having a compliance system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for automatically facilitating compliance with atleast one of a law, a regulation and a policy related to a lendingtransaction.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having an Internet of Things data collection andmonitoring system for validating reliability of a guarantee for a loan.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a robotic process automation system fornegotiation of a set of terms and conditions for a loan.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a robotic process automation system for loancollection.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a robotic process automation system forconsolidating a set of loans.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a robotic process automation system for managing afactoring loan.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a robotic process automation system for brokeringa mortgage loan.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a crowdsourcing and automated classificationsystem for validating condition of an issuer for a bond.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a social network monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having an Internet of Things data collection andmonitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a system that varies the terms and conditions of asubsidized loan based on a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a system that varies the terms and conditions of asubsidized loan based on a parameter monitored in a social network.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a system that varies the terms and conditions of asubsidized loan based on a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having an automated blockchain custody service formanaging a set of custodial assets.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having an underwriting system for a loan with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for underwriting lending entities andtransactions.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a loan marketing system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services andsmart contract services for marketing a loan to a set of prospectiveparties.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a rating system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for rating a set of loan-related entities.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system for validating the reliability of a guaranteefor a loan and having a compliance system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for automatically facilitating compliance with atleast one of a law, a regulation and a policy related to a lendingtransaction.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a robotic processautomation system for negotiation of a set of terms and conditions for aloan.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a robotic processautomation system for loan collection.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a robotic processautomation system for consolidating a set of loans.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a robotic processautomation system for managing a factoring loan.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a robotic processautomation system for brokering a mortgage loan.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a crowdsourcing andautomated classification system for validating condition of an issuerfor a bond.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a social networkmonitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having an Internet of Thingsdata collection and monitoring system with artificial intelligence forclassifying a condition about a bond.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a system that variesthe terms and conditions of a subsidized loan based on a parametermonitored by the IoT.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a system that variesthe terms and conditions of a subsidized loan based on a parametermonitored in a social network.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a system that variesthe terms and conditions of a subsidized loan based on a parametermonitored by crowdsourcing.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having an automated blockchaincustody service for managing a set of custodial assets.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having an underwriting systemfor a loan with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a loan marketing systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices and smart contract services for marketing a loan to a set ofprospective parties.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system for validatingreliability of a guarantee for a loan and having a compliance systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation and a policy relatedto a lending transaction.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a robotic process automation system forloan collection.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a robotic process automation system forconsolidating a set of loans.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a robotic process automation system formanaging a factoring loan.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a robotic process automation system forbrokering a mortgage loan.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a crowdsourcing and automatedclassification system for validating condition of an issuer for a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a social network monitoring system withartificial intelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having an Internet of Things data collectionand monitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a system that varies the terms andconditions of a subsidized loan based on a parameter monitored by theIoT.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a system that varies the terms andconditions of a subsidized loan based on a parameter monitored in asocial network.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a system that varies the terms andconditions of a subsidized loan based on a parameter monitored bycrowdsourcing.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having an automated blockchain custody servicefor managing a set of custodial assets.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having an underwriting system for a loan witha set of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for underwriting lending entitiesand transactions.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a loan marketing system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services andsmart contract services for marketing a loan to a set of prospectiveparties.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a rating system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for rating a set of loan-related entities.

In embodiments a lending platform is provided herein having a roboticprocess automation system for negotiation of a set of terms andconditions for a loan and having a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation and a policy related to a lendingtransaction.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a roboticprocess automation system for consolidating a set of loans.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a roboticprocess automation system for managing a factoring loan.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a roboticprocess automation system for brokering a mortgage loan.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a crowdsourcingand automated classification system for validating condition of anissuer for a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having an Internet ofThings data collection and monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored by the IoT.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored in a social network.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a system thatvaries the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having an automatedblockchain custody service for managing a set of custodial assets.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having an underwritingsystem for a loan with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a loanmarketing system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services and smart contract services for marketing a loanto a set of prospective parties.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a rating systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities.

In embodiments a lending platform is provided herein having a roboticprocess automation system for loan collection and having a compliancesystem with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for automaticallyfacilitating compliance with at least one of a law, a regulation and apolicy related to a lending transaction.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having arobotic process automation system for managing a factoring loan.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having arobotic process automation system for brokering a mortgage loan.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having asocial network monitoring system with artificial intelligence forclassifying a condition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having anInternet of Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored in a social network.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having anautomated blockchain custody service for managing a set of custodialassets.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having anunderwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having aloan marketing system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services and smart contract services formarketing a loan to a set of prospective parties.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having arating system with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities.

In embodiments a lending platform is provided herein having a roboticprocess automation system for consolidating a set of loans and having acompliance system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for automaticallyfacilitating compliance with at least one of a law, a regulation and apolicy related to a lending transaction.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having arobotic process automation system for brokering a mortgage loan.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having asocial network monitoring system with artificial intelligence forclassifying a condition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having anInternet of Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored in a social network.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having anautomated blockchain custody service for managing a set of custodialassets.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having anunderwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having aloan marketing system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services and smart contract services formarketing a loan to a set of prospective parties.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having arating system with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities.

In embodiments a lending platform is provided herein having a roboticprocess automation system for managing a factoring loan and having acompliance system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for automaticallyfacilitating compliance with at least one of a law, a regulation and apolicy related to a lending transaction.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having asocial network monitoring system with artificial intelligence forclassifying a condition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having anInternet of Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored in a social network.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having anautomated blockchain custody service for managing a set of custodialassets.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having anunderwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having aloan marketing system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services and smart contract services formarketing a loan to a set of prospective parties.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having arating system with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities.

In embodiments a lending platform is provided herein having a roboticprocess automation system for brokering a mortgage loan and having acompliance system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for automaticallyfacilitating compliance with at least one of a law, a regulation and apolicy related to a lending transaction.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having a social network monitoringsystem with artificial intelligence for classifying a condition about abond.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having an Internet of Things datacollection and monitoring system with artificial intelligence forclassifying a condition about a bond.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having a system that varies theterms and conditions of a subsidized loan based on a parameter monitoredby the IoT.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having a system that varies theterms and conditions of a subsidized loan based on a parameter monitoredin a social network.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having a system that varies theterms and conditions of a subsidized loan based on a parameter monitoredby crowdsourcing.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having an automated blockchaincustody service for managing a set of custodial assets.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having an underwriting system fora loan with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having a loan marketing systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices and smart contract services for marketing a loan to a set ofprospective parties.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having a rating system with a setof data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities.

In embodiments a lending platform is provided herein having acrowdsourcing and automated classification system for validatingcondition of an issuer for a bond and having a compliance system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation and a policy relatedto a lending transaction.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond and having an Internet of Things data collectionand monitoring system with artificial intelligence for classifying acondition about a bond.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond and having a system that varies the terms andconditions of a subsidized loan based on a parameter monitored by theIoT.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond and having a system that varies the terms andconditions of a subsidized loan based on a parameter monitored in asocial network.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond and having a system that varies the terms andconditions of a subsidized loan based on a parameter monitored bycrowdsourcing.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond and having an automated blockchain custodyservice for managing a set of custodial assets.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond and having an underwriting system for a loan witha set of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for underwriting lending entitiesand transactions.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond and having a loan marketing system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services andsmart contract services for marketing a loan to a set of prospectiveparties.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond and having a rating system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for rating a set of loan-related entities.

In embodiments a lending platform is provided herein having a socialnetwork monitoring system with artificial intelligence for classifying acondition about a bond and having a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation and a policy related to a lendingtransaction.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by the IoT.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored in a social network.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond and having asystem that varies the terms and conditions of a subsidized loan basedon a parameter monitored by crowdsourcing.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond and having anautomated blockchain custody service for managing a set of custodialassets.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond and having anunderwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond and having a loanmarketing system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services and smart contract services for marketing a loanto a set of prospective parties.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond and having arating system with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities.

In embodiments a lending platform is provided herein having an Internetof Things data collection and monitoring system with artificialintelligence for classifying a condition about a bond and having acompliance system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for automaticallyfacilitating compliance with at least one of a law, a regulation and apolicy related to a lending transaction.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by the IoT and having a system that varies the termsand conditions of a subsidized loan based on a parameter monitored in asocial network.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by the IoT and having a system that varies the termsand conditions of a subsidized loan based on a parameter monitored bycrowdsourcing.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by the IoT and having an automated blockchaincustody service for managing a set of custodial assets.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by the IoT and having an underwriting system for aloan with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by the IoT and having a loan marketing system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices and smart contract services for marketing a loan to a set ofprospective parties.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by the IoT and having a rating system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for rating a set of loan-related entities.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by the IoT and having a compliance system with a setof data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation and a policy relatedto a lending transaction.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored in a social network and having a system that variesthe terms and conditions of a subsidized loan based on a parametermonitored by crowdsourcing.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored in a social network and having an automatedblockchain custody service for managing a set of custodial assets.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored in a social network and having an underwritingsystem for a loan with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored in a social network and having a loan marketingsystem with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services and smart contract services for marketing a loanto a set of prospective parties.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored in a social network and having a rating system witha set of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored in a social network and having a compliance systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation and a policy relatedto a lending transaction.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing and having an automated blockchaincustody service for managing a set of custodial assets.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing and having an underwriting systemfor a loan with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for underwritinglending entities and transactions.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing and having a loan marketing systemwith a set of data-integrated microservices including data collectionand monitoring services, blockchain services, artificial intelligenceservices and smart contract services for marketing a loan to a set ofprospective parties.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing and having a rating system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for rating a set of loan-relatedentities.

In embodiments a lending platform is provided herein having a systemthat varies the terms and conditions of a subsidized loan based on aparameter monitored by crowdsourcing and having a compliance system witha set of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation and a policy relatedto a lending transaction.

In embodiments a lending platform is provided herein having an automatedblockchain custody service for managing a set of custodial assets andhaving an underwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions.

In embodiments a lending platform is provided herein having an automatedblockchain custody service for managing a set of custodial assets andhaving a loan marketing system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services and smart contractservices for marketing a loan to a set of prospective parties.

In embodiments a lending platform is provided herein having an automatedblockchain custody service for managing a set of custodial assets andhaving a rating system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services, and smart contract services for ratinga set of loan-related entities.

In embodiments a lending platform is provided herein having an automatedblockchain custody service for managing a set of custodial assets andhaving a compliance system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services, and smart contract services forautomatically facilitating compliance with at least one of a law, aregulation and a policy related to a lending transaction.

In embodiments a lending platform is provided herein having anunderwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions andhaving a loan marketing system with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services and smart contractservices for marketing a loan to a set of prospective parties.

In embodiments a lending platform is provided herein having anunderwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions andhaving a rating system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services, and smart contract services for ratinga set of loan-related entities.

In embodiments a lending platform is provided herein having anunderwriting system for a loan with a set of data-integratedmicroservices including data collection and monitoring services,blockchain services, artificial intelligence services, and smartcontract services for underwriting lending entities and transactions andhaving a compliance system with a set of data-integrated microservicesincluding data collection and monitoring services, blockchain services,artificial intelligence services, and smart contract services forautomatically facilitating compliance with at least one of a law, aregulation and a policy related to a lending transaction.

In embodiments a lending platform is provided herein having a loanmarketing system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services and smart contract services for marketing a loanto a set of prospective parties and having a rating system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for rating a set of loan-related entities.

In embodiments a lending platform is provided herein having a loanmarketing system with a set of data-integrated microservices includingdata collection and monitoring services, blockchain services, artificialintelligence services and smart contract services for marketing a loanto a set of prospective parties and having a compliance system with aset of data-integrated microservices including data collection andmonitoring services, blockchain services, artificial intelligenceservices, and smart contract services for automatically facilitatingcompliance with at least one of a law, a regulation and a policy relatedto a lending transaction.

In embodiments a lending platform is provided herein having a ratingsystem with a set of data-integrated microservices including datacollection and monitoring services, blockchain services, artificialintelligence services, and smart contract services for rating a set ofloan-related entities and having a compliance system with a set ofdata-integrated microservices including data collection and monitoringservices, blockchain services, artificial intelligence services, andsmart contract services for automatically facilitating compliance withat least one of a law, a regulation and a policy related to a lendingtransaction.

In embodiments, a database service may be provided herein that embodies,enables, or is associated with a blockchain, ledger, such as adistributed ledger, or the like, such as in connection with any of theembodiments described herein or in the document incorporated byreference that refer to them. In embodiments the database service maycomprise a transparent, immutable, and cryptographically verifiableledger database service, such as the Amazon™ QLDB™ database service. Thedatabase service may be included within one or connected with or more ofthe layers or microservices of a lending enablement platform 100, suchas the adaptive intelligent systems 158 layer or the data storage layer168. The service may be used, for example, in connection with acentralized ledger that records all changes or transactions andmaintains an immutable record of these changes, such as by tracing anentity through various environments or processes, tracking the historyof debits and credits in a series of transactions, or validating factsrelevant to an underwriting process, a claim, or a legal or regulatoryproceeding. A ledger may be owned by a single trusted entity or set oftrusted entities and may be shared with any other entities, such as onesthat working together in a coordinated process, such as a transaction, aproduction process, a joint service, or many others. As compared to arelational database, the database service may provide immutable,cryptographically verifiable ledger entries, without the need for customaudit tables or trails. As compared to a blockchain framework, such adatabase service may include capabilities to perform queries, createtables, index data, and the like. The database service may optionallyomit requirements for many blockchain frameworks that slow performance,such as requirement of consensus before committing transactions, or thedatabase service may employ optional consensus features. In embodiments,the database service may comprise transparent, immutable, andcryptographically verifiable ledger that users can use to buildapplications that act as a system of record, where multiple parties aretransacting within a centralized, trusted entity or set of entities. Thedatabase service may complement or substitute for the building auditfunctionality into a relational database or for using conventionaldistributed ledger capabilities in a blockchain framework. The databaseservice may use an immutable transactional log or journal, which maytrack each application data change and maintain a comprehensive andverifiable history of changes. In embodiments, transactions may beconfigured to comply with requirements of atomicity, consistency,isolation, and durability (ACID) to be logged in the log or journal,which is configured to prevent deletions or modifications. Changes maybe cryptographically chained, such that they are auditable andverifiable, such as in a history that users can query or analyze, suchas using conventional query types, such as SQL queries. In embodiments,the database service may be provided in a serverless form, such thatthere is no need to provision specific server capacity or to configureread/write limits. To initiate the database service, the user can createa ledger, define tables, and the like, and the database service willautomatically scale to support application demands. In contrast toblockchain-based ledgers, a database service may omit requirements for adistributed consensus, so it can execute more transactions in the sametime.

In embodiments of the present disclosure that refer to a blockchain ordistributed ledger, a managed blockchain service may be used, such asthe Amazon™ Managed Blockchain™, which may comprise a facility forconvenient creation and management of a scaled blockchain network. Themanaged blockchain service may be provided as part of a layered dataservices architecture as described in this disclosure. In situationswhere users want immutable and verifiable capability provided by ablockchain or ledger, they may also seek the ability to allow multipleparties to transact, execute contracts (such as in smart contractembodiments described herein), share data, and the like without atrusted central authority. As setting up conventional blockchainframeworks requires significant time and technical expertise, where eachparticipant in a permissioned network has to provision hardware, installsoftware, create, and manage certificates for access control, andconfigure network settings. As a given blockchain application grows,there is also activity required to scale the network, monitor resourcesacross blockchain nodes, add or remove hardware and manage networkavailability. In embodiments, a managed blockchain service may providefor management of each of these requirements and enabling capabilities.This may include supporting open source blockchain frameworks andenabling selection, setup and deployment of a selected framework in adashboard, console, or other user interface, wherein users may choosetheir preferred framework, add network members, and configure membernodes that will process transaction requests. The managed blockchainservice may then automatically create a blockchain network, such as onethat can span multiple accounts with multiple nodes per member, andconfigure software, security, and network settings. The managedblockchain service may secure and manage network certificates, such aswith a key management service, which may allow customer management ofthe keys. In embodiments, the managed blockchain service may include oneor more APIs, such as a voting API, such as one that allows networkmembers to vote, such as to vote to add or remove members. Asapplication usage grows for a given application (such as any of thenoted applications described in connection with the lending enablementplatform 100), users can add more capacity to the blockchain network,such as with a simple API call. In embodiments, the managed blockchainservice may be provided with a range of combinations of compute andmemory capacity, such as to give users the ability to choose the rightmix of resources for a given blockchain-based application.

Referring to FIGS. 4-31, in embodiments of the present disclosure,including ones involving artificial intelligence 156, adaptiveintelligent systems 158, robotic process automation 154, expert systems,self-organization, machine learning, training of models, and the like,may benefit from the use of a neural net, such as a neural net trainedfor pattern recognition, for prediction, for optimization based on a setof desired outcomes, for classification or recognition of one or moreparameters, features characteristics, or phenomena, for support ofautonomous control, and other purposes. References to artificialintelligence, expert systems, models, adaptive intelligence, and/orneural networks throughout this disclosure should be understood tooptionally encompass use of a wide range of different types of neuralnetworks, machine learning systems, artificial intelligence systems, andthe like as particular embodiments permit, such as feed forward neuralnetworks, radial basis function neural networks, self-organizing neuralnetworks (e.g., Kohonen self-organizing neural networks), recurrentneural networks, modular neural networks, artificial neural networks,physical neural networks, multi-layered neural networks, convolutionalneural networks, hybrids of neural networks with other expert systems(e.g., hybrid fuzzy logic—neural network systems), Autoencoder neuralnetworks, probabilistic neural networks, time delay neural networks,convolutional neural networks, regulatory feedback neural networks,radial basis function neural networks, recurrent neural networks,Hopfield neural networks, Boltzmann machine neural networks,self-organizing map (SOM) neural networks, learning vector quantization(LVQ) neural networks, fully recurrent neural networks, simple recurrentneural networks, echo state neural networks, long short-term memoryneural networks, bi-directional neural networks, hierarchical neuralnetworks, stochastic neural networks, genetic scale RNN neural networks,committee of machines neural networks, associative neural networks,physical neural networks, instantaneously trained neural networks,spiking neural networks, neocognitron neural networks, dynamic neuralnetworks, cascading neural networks, neuro-fuzzy neural networks,compositional pattern-producing neural networks, memory neural networks,hierarchical temporal memory neural networks, deep feed forward neuralnetworks, gated recurrent unit (GCU) neural networks, auto encoderneural networks, variational auto encoder neural networks, de-noisingauto encoder neural networks, sparse auto-encoder neural networks,Markov chain neural networks, restricted Boltzmann machine neuralnetworks, deep belief neural networks, deep convolutional neuralnetworks, de-convolutional neural networks, deep convolutional inversegraphics neural networks, generative adversarial neural networks, liquidstate machine neural networks, extreme learning machine neural networks,echo state neural networks, deep residual neural networks, supportvector machine neural networks, neural Turing machine neural networks,and/or holographic associative memory neural networks, or hybrids orcombinations of the foregoing, or combinations with other expertsystems, such as rule-based systems, model-based systems (including onesbased on physical models, statistical models, flow-based models,biological models, biomimetic models, and the like).

The foregoing neural networks may have a variety of nodes or neurons,which may perform a variety of functions on inputs, such as inputsreceived from sensors or other data sources, including other nodes.Functions may involve weights, features, feature vectors, and the like.Neurons may include perceptrons, neurons that mimic biological functions(such as of the human senses of touch, vision, taste, hearing, andsmell), and the like. Continuous neurons, such as with sigmoidalactivation, may be used in the context of various forms of neural net,such as where back propagation is involved.

In many embodiments, an expert system or neural network may be trained,such as by a human operator or supervisor, or based on a data set,model, or the like. Training may include presenting the neural networkwith one or more training data sets that represent values, such assensor data, event data, parameter data, and other types of data(including the many types described throughout this disclosure), as wellas one or more indicators of an outcome, such as an outcome of aprocess, an outcome of a calculation, an outcome of an event, an outcomeof an activity, or the like. Training may include training inoptimization, such as training a neural network to optimize one or moresystems based on one or more optimization approaches, such as Bayesianapproaches, parametric Bayes classifier approaches, k-nearest-neighborclassifier approaches, iterative approaches, interpolation approaches,Pareto optimization approaches, algorithmic approaches, and the like.Feedback may be provided in a process of variation and selection, suchas with a genetic algorithm that evolves one or more solutions based onfeedback through a series of rounds.

In embodiments, a plurality of neural networks may be deployed in acloud platform that receives data streams and other inputs collected(such as by mobile data collectors) in one or more transactionalenvironments and transmitted to the cloud platform over one or morenetworks, including using network coding to provide efficienttransmission. In the cloud platform, optionally using massively parallelcomputational capability, a plurality of different neural networks ofvarious types (including modular forms, structure-adaptive forms,hybrids, and the like) may be used to undertake prediction,classification, control functions, and provide other outputs asdescribed in connection with expert systems disclosed throughout thisdisclosure. The different neural networks may be structured to competewith each other (optionally including use evolutionary algorithms,genetic algorithms, or the like), such that an appropriate type ofneural network, with appropriate input sets, weights, node types andfunctions, and the like, may be selected, such as by an expert system,for a specific task involved in a given context, workflow, environmentprocess, system, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed forwardneural network, which moves information in one direction, such as from adata input, like a data source related to at least one resource orparameter related to a transactional environment, such as any of thedata sources mentioned throughout this disclosure, through a series ofneurons or nodes, to an output. Data may move from the input nodes tothe output nodes, optionally passing through one or more hidden nodes,without loops. In embodiments, feed forward neural networks may beconstructed with various types of units, such as binary McCulloch-Pittsneurons, the simplest of which is a perceptron.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a capsule neuralnetwork, such as for prediction, classification, or control functionswith respect to a transactional environment, such as relating to one ormore of the machines and automated systems described throughout thisdisclosure.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, which may be preferred in some situationsinvolving interpolation in a multi-dimensional space (such as whereinterpolation is helpful in optimizing a multi-dimensional function,such as for optimizing a data marketplace as described here, optimizingthe efficiency or output of a power generation system, a factory system,or the like, or other situation involving multiple dimensions. Inembodiments, each neuron in the RBF neural network stores an examplefrom a training set as a “prototype.” Linearity involved in thefunctioning of this neural network offers RBF the advantage of nottypically suffering from problems with local minima or maxima.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, such as one that employs a distancecriterion with respect to a center (e.g., a Gaussian function). A radialbasis function may be applied as a replacement for a hidden layer, suchas a sigmoidal hidden layer transfer, in a multi-layer perceptron. AnRBF network may have two layers, such as where an input is mapped ontoeach RBF in a hidden layer. In embodiments, an output layer may comprisea linear combination of hidden layer values representing, for example, amean predicted output. The output layer value may provide an output thatis the same as or similar to that of a regression model in statistics.In classification problems, the output layer may be a sigmoid functionof a linear combination of hidden layer values, representing a posteriorprobability. Performance in both cases is often improved by shrinkagetechniques, such as ridge regression in classical statistics. Thiscorresponds to a prior belief in small parameter values (and thereforesmooth output functions) in a Bayesian framework. RBF networks may avoidlocal minima, because the only parameters that are adjusted in thelearning process are the linear mapping from hidden layer to outputlayer. Linearity ensures that the error surface is quadratic andtherefore has a single minimum. In regression problems, this can befound in one matrix operation. In classification problems, the fixednon-linearity introduced by the sigmoid output function may be handledusing an iteratively re-weighted least squares function or the like.

RBF networks may use kernel methods such as support vector machines(SVM) and Gaussian processes (where the RBF is the kernel function). Anon-linear kernel function may be used to project the input data into aspace where the learning problem can be solved using a linear model.

In embodiments, an RBF neural network may include an input layer, ahidden layer and a summation layer. In the input layer, one neuronappears in the input layer for each predictor variable. In the case ofcategorical variables, N−1 neurons are used, where N is the number ofcategories. The input neurons may, in embodiments, standardize the valueranges by subtracting the median and dividing by the interquartilerange. The input neurons may then feed the values to each of the neuronsin the hidden layer. In the hidden layer, a variable number of neuronsmay be used (determined by the training process). Each neuron mayconsist of a radial basis function that is centered on a point with asmany dimensions as a number of predictor variables. The spread (e.g.,radius) of the RBF function may be different for each dimension. Thecenters and spreads may be determined by training. When presented with avector of input values from the input layer, a hidden neuron may computea Euclidean distance of the test case from the neuron's center point andthen apply the RBF kernel function to this distance, such as using thespread values. The resulting value may then be passed to the summationlayer. In the summation layer, the value coming out of a neuron in thehidden layer may be multiplied by a weight associated with the neuronand may add to the weighted values of other neurons. This sum becomesthe output. For classification problems, one output is produced (with aseparate set of weights and summation units) for each target category.The value output for a category is the probability that the case beingevaluated has that category. In training of an RBF, various parametersmay be determined, such as the number of neurons in a hidden layer, thecoordinates of the center of each hidden-layer function, the spread ofeach function in each dimension, and the weights applied to outputs asthey pass to the summation layer. Training may be used by clusteringalgorithms (such as k-means clustering), by evolutionary approaches, andthe like.

In embodiments, a recurrent neural network may have a time-varying,real-valued (more than just zero or one) activation (output). Eachconnection may have a modifiable real-valued weight. Some of the nodesare called labeled nodes, some output nodes, and others hidden nodes.For supervised learning in discrete time settings, training sequences ofreal-valued input vectors may become sequences of activations of theinput nodes, one input vector at a time. At each time step, eachnon-input unit may compute its current activation as a nonlinearfunction of the weighted sum of the activations of all units from whichit receives connections. The system can explicitly activate (independentof incoming signals) some output units at certain time steps.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingneural network, such as a Kohonen self-organizing neural network, suchas for visualization of views of data, such as low-dimensional views ofhigh-dimensional data. The self-organizing neural network may applycompetitive learning to a set of input data, such as from one or moresensors or other data inputs from or associated with a transactionalenvironment, including any machine or component that relates to thetransactional environment. In embodiments, the self-organizing neuralnetwork may be used to identify structures in data, such as unlabeleddata, such as in data sensed from a range of data sources about orsensors in or about in a transactional environment, where sources of thedata are unknown (such as where events may be coming from any of a rangeof unknown sources). The self-organizing neural network may organizestructures or patterns in the data, such that they can be recognized,analyzed, and labeled, such as identifying market behavior structures ascorresponding to other events and signals.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a recurrent neuralnetwork, which may allow for a bi-directional flow of data, such aswhere connected units (e.g., neurons or nodes) form a directed cycle.Such a network may be used to model or exhibit dynamic temporalbehavior, such as involved in dynamic systems, such as a wide variety ofthe automation systems, machines and devices described throughout thisdisclosure, such as an automated agent interacting with a marketplacefor purposes of collecting data, testing spot market transactions,execution transactions, and the like, where dynamic system behaviorinvolves complex interactions that a user may desire to understand,predict, control and/or optimize. For example, the recurrent neuralnetwork may be used to anticipate the state of a market, such as oneinvolving a dynamic process or action, such as a change in state of aresource that is traded in or that enables a marketplace oftransactional environment. In embodiments, the recurrent neural networkmay use internal memory to process a sequence of inputs, such as fromother nodes and/or from sensors and other data inputs from or about thetransactional environment, of the various types described herein. Inembodiments, the recurrent neural network may also be used for patternrecognition, such as for recognizing a machine, component, agent, orother item based on a behavioral signature, a profile, a set of featurevectors (such as in an audio file or image), or the like. In anon-limiting example, a recurrent neural network may recognize a shiftin an operational mode of a marketplace or machine by learning toclassify the shift from a training data set consisting of a stream ofdata from one or more data sources of sensors applied to or about one ormore resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a modular neuralnetwork, which may comprise a series of independent neural networks(such as ones of various types described herein) that are moderated byan intermediary. Each of the independent neural networks in the modularneural network may work with separate inputs, accomplishing subtasksthat make up the task the modular network as whole is intended toperform. For example, a modular neural network may comprise a recurrentneural network for pattern recognition, such as to recognize what typeof machine or system is being sensed by one or more sensors that areprovided as input channels to the modular network and an RBF neuralnetwork for optimizing the behavior of the machine or system onceunderstood. The intermediary may accept inputs of each of the individualneural networks, process them, and create output for the modular neuralnetwork, such an appropriate control parameter, a prediction of state,or the like.

Combinations among any of the pairs, triplets, or larger combinations,of the various neural network types described herein, are encompassed bythe present disclosure. This may include combinations where an expertsystem uses one neural network for recognizing a pattern (e.g., apattern indicating a problem or fault condition) and a different neuralnetwork for self-organizing an activity or workflow based on therecognized pattern (such as providing an output governing autonomouscontrol of a system in response to the recognized condition or pattern).This may also include combinations where an expert system uses oneneural network for classifying an item (e.g., identifying a machine, acomponent, or an operational mode) and a different neural network forpredicting a state of the item (e.g., a fault state, an operationalstate, an anticipated state, a maintenance state, or the like). Modularneural networks may also include situations where an expert system usesone neural network for determining a state or context (such as a stateof a machine, a process, a work flow, a marketplace, a storage system, anetwork, a data collector, or the like) and a different neural networkfor self-organizing a process involving the state or context (e.g., adata storage process, a network coding process, a network selectionprocess, a data marketplace process, a power generation process, amanufacturing process, a refining process, a digging process, a boringprocess, or other process described herein).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a physical neuralnetwork where one or more hardware elements is used to perform orsimulate neural behavior. In embodiments, one or more hardware neuronsmay be configured to stream voltage values, current values, or the likethat represent sensor data, such as to calculate information from analogsensor inputs representing energy consumption, energy production, or thelike, such as by one or more machines providing energy or consumingenergy for one or more transactions. One or more hardware nodes may beconfigured to stream output data resulting from the activity of theneural net. Hardware nodes, which may comprise one or more chips,microprocessors, integrated circuits, programmable logic controllers,application-specific integrated circuits, field-programmable gatearrays, or the like, may be provided to optimize the machine that isproducing or consuming energy, or to optimize another parameter of somepart of a neural net of any of the types described herein. Hardwarenodes may include hardware for acceleration of calculations (such asdedicated processors for performing basic or more sophisticatedcalculations on input data to provide outputs, dedicated processors forfiltering or compressing data, dedicated processors for de-compressingdata, dedicated processors for compression of specific file or datatypes (e.g., for handling image data, video streams, acoustic signals,thermal images, heat maps, or the like), and the like. A physical neuralnetwork may be embodied in a data collector, including one that may bereconfigured by switching or routing inputs in varying configurations,such as to provide different neural net configurations within the datacollector for handling different types of inputs (with the switching andconfiguration optionally under control of an expert system, which mayinclude a software-based neural net located on the data collector orremotely). A physical, or at least partially physical, neural networkmay include physical hardware nodes located in a storage system, such asfor storing data within a machine, a data storage system, a distributedledger, a mobile device, a server, a cloud resource, or in atransactional environment, such as for accelerating input/outputfunctions to one or more storage elements that supply data to or takedata from the neural net. A physical, or at least partially physical,neural network may include physical hardware nodes located in a network,such as for transmitting data within, to or from an industrialenvironment, such as for accelerating input/output functions to one ormore network nodes in the net, accelerating relay functions, or thelike. In embodiments of a physical neural network, an electricallyadjustable resistance material may be used for emulating the function ofa neural synapse. In embodiments, the physical hardware emulates theneurons, and software emulates the neural network between the neurons.In embodiments, neural networks complement conventional algorithmiccomputers. They are versatile and can be trained to perform appropriatefunctions without the need for any instructions, such as classificationfunctions, optimization functions, pattern recognition functions,control functions, selection functions, evolution functions, and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a multilayeredfeed forward neural network, such as for complex pattern classificationof one or more items, phenomena, modes, states, or the like. Inembodiments, a multilayered feed forward neural network may be trainedby an optimization technical, such as a genetic algorithm, such as toexplore a large and complex space of options to find an optimum, ornear-optimum, global solution. For example, one or more geneticalgorithms may be used to train a multilayered feed forward neuralnetwork to classify complex phenomena, such as to recognize complexoperational modes of machines, such as modes involving complexinteractions among machines (including interference effects, resonanceeffects, and the like), modes involving non-linear phenomena, modesinvolving critical faults, such as where multiple, simultaneous faultsoccur, making root cause analysis difficult, and others. In embodiments,a multilayered feed forward neural network may be used to classifyresults from monitoring of a marketplace, such as monitoring systems,such as automated agents, that operate within the marketplace, as wellas monitoring resources that enable the marketplace, such as computing,networking, energy, data storage, energy storage, and other resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed-forward,back-propagation multi-layer perceptron (MLP) neural network, such asfor handling one or more remote sensing applications, such as for takinginputs from sensors distributed throughout various transactionalenvironments. In embodiments, the MLP neural network may be used forclassification of transactional environments and resource environments,such as lending markets, spot markets, forward markets, energy markets,renewable energy credit (REC) markets, networking markets, advertisingmarkets, spectrum markets, ticketing markets, rewards markets, computemarkets, and others mentioned throughout this disclosure, as well asphysical resources and environments that produce them, such as energyresources (including renewable energy environments, mining environments,exploration environments, drilling environments, and the like, includingclassification of geological structures (including underground featuresand above ground features), classification of materials (includingfluids, minerals, metals, and the like), and other problems. This mayinclude fuzzy classification.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use astructure-adaptive neural network, where the structure of a neuralnetwork is adapted, such as based on a rule, a sensed condition, acontextual parameter, or the like. For example, if a neural network doesnot converge on a solution, such as classifying an item or arriving at aprediction, when acting on a set of inputs after some amount oftraining, the neural network may be modified, such as from a feedforward neural network to a recurrent neural network, such as byswitching data paths between some subset of nodes from unidirectional tobi-directional data paths. The structure adaptation may occur undercontrol of an expert system, such as to trigger adaptation uponoccurrence of a trigger, rule or event, such as recognizing occurrenceof a threshold (such as an absence of a convergence to a solution withina given amount of time) or recognizing a phenomenon as requiringdifferent or additional structure (such as recognizing that a system isvarying dynamically or in a non-linear fashion). In one non-limitingexample, an expert system may switch from a simple neural networkstructure like a feed forward neural network to a more complex neuralnetwork structure like a recurrent neural network, a convolutionalneural network, or the like upon receiving an indication that acontinuously variable transmission is being used to drive a generator,turbine, or the like in a system being analyzed.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an autoencoder,autoassociator or Diabolo neural network, which may be similar to amultilayer perceptron (MLP) neural network, such as where there may bean input layer, an output layer and one or more hidden layers connectingthem. However, the output layer in the auto-encoder may have the samenumber of units as the input layer, where the purpose of the MLP neuralnetwork is to reconstruct its own inputs (rather than just emitting atarget value). Therefore, the auto encoders may operate as anunsupervised learning model. An auto encoder may be used, for example,for unsupervised learning of efficient codings, such as fordimensionality reduction, for learning generative models of data, andthe like. In embodiments, an auto-encoding neural network may be used toself-learn an efficient network coding for transmission of analog sensordata from a machine over one or more networks or of digital data fromone or more data sources. In embodiments, an auto-encoding neuralnetwork may be used to self-learn an efficient storage approach forstorage of streams of data.

al environment. In embodiments, methods and systems described hereinthat involve an expert system or self-organization capability may use aprobabilistic neural network (PNN), which in embodiments may comprise amulti-layer (e.g., four-layer) feed forward neural network, where layersmay include input layers, hidden layers, pattern/summation layers and anoutput layer. In an embodiment of a PNN algorithm, a parent probabilitydistribution function (PDF) of each class may be approximated, such asby a Parzen window and/or a non-parametric function. Then, using the PDFof each class, the class probability of a new input is estimated, andBayes' rule may be employed, such as to allocate it to the class withthe highest posterior probability. A PNN may embody a Bayesian networkand may use a statistical algorithm or analytic technique, such asKernel Fisher discriminant analysis technique. The PNN may be used forclassification and pattern recognition in any of a wide range ofembodiments disclosed herein. In one non-limiting example, aprobabilistic neural network may be used to predict a fault condition ofan engine based on collection of data inputs from sensors andinstruments for the engine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a time delayneural network (TDNN), which may comprise a feed forward architecturefor sequential data that recognizes features independent of sequenceposition. In embodiments, to account for time shifts in data, delays areadded to one or more inputs, or between one or more nodes, so thatmultiple data points (from distinct points in time) are analyzedtogether. A time delay neural network may form part of a larger patternrecognition system, such as using a perceptron network. In embodiments,a TDNN may be trained with supervised learning, such as where connectionweights are trained with back propagation or under feedback. Inembodiments, a TDNN may be used to process sensor data from distinctstreams, such as a stream of velocity data, a stream of accelerationdata, a stream of temperature data, a stream of pressure data, and thelike, where time delays are used to align the data streams in time, suchas to help understand patterns that involve understanding of the variousstreams (e.g., changes in price patterns in spot or forward markets).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a convolutionalneural network (referred to in some cases as a CNN, a ConvNet, a shiftinvariant neural network, or a space invariant neural network), whereinthe units are connected in a pattern similar to the visual cortex of thehuman brain. Neurons may respond to stimuli in a restricted region ofspace, referred to as a receptive field. Receptive fields may partiallyoverlap, such that they collectively cover the entire (e.g., visual)field. Node responses can be calculated mathematically, such as by aconvolution operation, such as using multilayer perceptrons that useminimal preprocessing. A convolutional neural network may be used forrecognition within images and video streams, such as for recognizing atype of machine in a large environment using a camera system disposed ona mobile data collector, such as on a drone or mobile robot. Inembodiments, a convolutional neural network may be used to provide arecommendation based on data inputs, including sensor inputs and othercontextual information, such as recommending a route for a mobile datacollector. In embodiments, a convolutional neural network may be usedfor processing inputs, such as for natural language processing ofinstructions provided by one or more parties involved in a workflow inan environment. In embodiments, a convolutional neural network may bedeployed with a large number of neurons (e.g., 100,000, 500,000 ormore), with multiple (e.g., 4, 5, 6 or more) layers, and with many(e.g., millions) of parameters. A convolutional neural net may use oneor more convolutional nets.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a regulatoryfeedback network, such as for recognizing emergent phenomena (such asnew types of behavior not previously understood in a transactionalenvironment).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingmap (SOM), involving unsupervised learning. A set of neurons may learnto map points in an input space to coordinates in an output space. Theinput space can have different dimensions and topology from the outputspace, and the SOM may preserve these while mapping phenomena intogroups.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a learning vectorquantization neural net (LVQ). Prototypical representatives of theclasses may parameterize, together with an appropriate distance measure,in a distance-based classification scheme.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an echo statenetwork (ESN), which may comprise a recurrent neural network with asparsely connected, random hidden layer. The weights of output neuronsmay be changed (e.g., the weights may be trained based on feedback). Inembodiments, an ESN may be used to handle time series patterns, such as,in an example, recognizing a pattern of events associated with a market,such as pattern of price changes in response to stimuli.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a Bi-directional,recurrent neural network (BRNN), such as using a finite sequence ofvalues (e.g., voltage values from a sensor) to predict or label eachelement of the sequence based on both the past and the future context ofthe element. This may be done by adding the outputs of two RNNs, such asone processing the sequence from left to right, the other one from rightto left. The combined outputs are the predictions of target signals,such as ones provided by a teacher or supervisor. A bi-directional RNNmay be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchical RNNthat connects elements in various ways to decompose hierarchicalbehavior, such as into useful subprograms. In embodiments, ahierarchical RNN may be used to manage one or more hierarchicaltemplates for data collection in a transactional environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a stochasticneural network, which may introduce random variations into the network.Such random variations can be viewed as a form of statistical sampling,such as Monte Carlo sampling.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a genetic scalerecurrent neural network. In such embodiments, a RNN (often a LSTM) isused where a series is decomposed into a number of scales where everyscale informs the primary length between two consecutive points. A firstorder scale consists of a normal RNN, a second order consists of allpoints separated by two indices and so on. The Nth order RNN connectsthe first and last node. The outputs from all the various scales may betreated as a committee of members, and the associated scores may be usedgenetically for the next iteration.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a committee ofmachines (CoM), comprising a collection of different neural networksthat together “vote” on a given example. Because neural networks maysuffer from local minima, starting with the same architecture andtraining, but using randomly different initial weights often givesdifferent results. A CoM tends to stabilize the result.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an associativeneural network (ASNN), such as involving an extension of committee ofmachines that combines multiple feed forward neural networks and ak-nearest neighbor technique. It may use the correlation betweenensemble responses as a measure of distance amid the analyzed cases forthe kNN. This corrects the bias of the neural network ensemble. Anassociative neural network may have a memory that can coincide with atraining set. If new data become available, the network instantlyimproves its predictive ability and provides data approximation(self-learns) without retraining. Another important feature of ASNN isthe possibility to interpret neural network results by analysis ofcorrelations between data cases in the space of models.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an instantaneouslytrained neural network (ITNN), where the weights of the hidden and theoutput layers are mapped directly from training vector data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a spiking neuralnetwork, which may explicitly consider the timing of inputs. The networkinput and output may be represented as a series of spikes (such as adelta function or more complex shapes). SNNs can process information inthe time domain (e.g., signals that vary over time, such as signalsinvolving dynamic behavior of markets or transactional environments).They are often implemented as recurrent networks.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a dynamic neuralnetwork that addresses nonlinear multivariate behavior and includeslearning of time-dependent behavior, such as transient phenomena anddelay effects. Transients may include behavior of shifting marketvariables, such as prices, available quantities, availablecounterparties, and the like.

In embodiments, cascade correlation may be used as an architecture andsupervised learning algorithm, supplementing adjustment of the weightsin a network of fixed topology. Cascade-correlation may begin with aminimal network, then automatically trains and add new hidden units oneby one, creating a multi-layer structure. Once a new hidden unit hasbeen added to the network, its input-side weights may be frozen. Thisunit then becomes a permanent feature-detector in the network, availablefor producing outputs or for creating other, more complex featuredetectors. The cascade-correlation architecture may learn quickly,determine its own size and topology, and retain the structures it hasbuilt even if the training set changes and requires no back-propagation.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a neuro-fuzzynetwork, such as involving a fuzzy inference system in the body of anartificial neural network. Depending on the type, several layers maysimulate the processes involved in a fuzzy inference, such asfuzzification, inference, aggregation and defuzzification. Embedding afuzzy system in a general structure of a neural net as the benefit ofusing available training methods to find the parameters of a fuzzysystem.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a compositionalpattern-producing network (CPPN), such as a variation of an associativeneural network (ANN) that differs the set of activation functions andhow they are applied. While typical ANNs often contain only sigmoidfunctions (and sometimes Gaussian functions), CPPNs can include bothtypes of functions and many others. Furthermore, CPPNs may be appliedacross the entire space of possible inputs, so that they can represent acomplete image. Since they are compositions of functions, CPPNs ineffect encode images at infinite resolution and can be sampled for aparticular display at whatever resolution is optimal.

This type of network can add new patterns without re-training. Inembodiments, methods and systems described herein that involve an expertsystem or self-organization capability may use a one-shot associativememory network, such as by creating a specific memory structure, whichassigns each new pattern to an orthogonal plane using adjacentlyconnected hierarchical arrays.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchicaltemporal memory (HTM) neural network, such as involving the structuraland algorithmic properties of the neocortex. HTM may use a biomimeticmodel based on memory-prediction theory. HTM may be used to discover andinfer the high-level causes of observed input patterns and sequences.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a holographicassociative memory (HAM) neural network, which may comprise an analog,correlation-based, associative, stimulus-response system. Informationmay be mapped onto the phase orientation of complex numbers. The memoryis effective for associative memory tasks, generalization and patternrecognition with changeable attention.

In embodiments, various embodiments involving network coding may be usedto code transmission data among network nodes in neural net, such aswhere nodes are located in one or more data collectors or machines in atransactional environment.

Referring to FIG. 22 through FIG. 49, embodiments of the presentdisclosure, including ones involving expert systems, self-organization,machine learning, artificial intelligence, and the like, may benefitfrom the use of a neural net, such as a neural net trained for patternrecognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes. References to a neural net throughout this disclosureshould be understood to encompass a wide range of different types ofneural networks, machine learning systems, artificial intelligencesystems, and the like, such as feed forward neural networks, radialbasis function neural networks, self-organizing neural networks (e.g.,Kohonen self-organizing neural networks), recurrent neural networks,modular neural networks, artificial neural networks, physical neuralnetworks, multi-layered neural networks, convolutional neural networks,hybrids of neural networks with other expert systems (e.g., hybrid fuzzylogic—neural network systems), Autoencoder neural networks,probabilistic neural networks, time delay neural networks, convolutionalneural networks, regulatory feedback neural networks, radial basisfunction neural networks, recurrent neural networks, Hopfield neuralnetworks, Boltzmann machine neural networks, self-organizing map (SOM)neural networks, learning vector quantization (LVQ) neural networks,fully recurrent neural networks, simple recurrent neural networks, echostate neural networks, long short-term memory neural networks,bi-directional neural networks, hierarchical neural networks, stochasticneural networks, genetic scale RNN neural networks, committee ofmachines neural networks, associative neural networks, physical neuralnetworks, instantaneously trained neural networks, spiking neuralnetworks, neocognitron neural networks, dynamic neural networks,cascading neural networks, neuro-fuzzy neural networks, compositionalpattern-producing neural networks, memory neural networks, hierarchicaltemporal memory neural networks, deep feed forward neural networks,gated recurrent unit (GCU) neural networks, auto encoder neuralnetworks, variational auto encoder neural networks, de-noising autoencoder neural networks, sparse auto-encoder neural networks, Markovchain neural networks, restricted Boltzmann machine neural networks,deep belief neural networks, deep convolutional neural networks,de-convolutional neural networks, deep convolutional inverse graphicsneural networks, generative adversarial neural networks, liquid statemachine neural networks, extreme learning machine neural networks, echostate neural networks, deep residual neural networks, support vectormachine neural networks, neural Turing machine neural networks, and/orholographic associative memory neural networks, or hybrids orcombinations of the foregoing, or combinations with other expertsystems, such as rule-based systems, model-based systems (including onesbased on physical models, statistical models, flow-based models,biological models, biomimetic models, and the like).

In embodiments, FIGS. 23 through 49 depict exemplary neural networks andFIG. 22 depicts a legend showing the various components of the neuralnetworks depicted throughout FIGS. 23 to 49. FIG. 22 depicts variousneural net components depicted in cells that are assigned functions andrequirements. In embodiments, the various neural net examples mayinclude (from top to bottom in the example of FIG. 22): back feddata/sensor input cells, data/sensor input cells, noisy input cells, andhidden cells. The neural net components also include probabilistichidden cells, spiking hidden cells, output cells, match input/outputcells, recurrent cells, memory cells, different memory cells, kernels,and convolution or pool cells.

In embodiments, FIG. 23 depicts an exemplary perceptron neural networkthat may connect to, integrate with, or interface with the platform 100.The platform may also be associated with further neural net systems suchas a feed forward neural network (FIG. 24), a radial basis neuralnetwork (FIG. 25), a deep feed forward neural network (FIG. 26), arecurrent neural network (FIG. 27), a long/short term neural network(FIG. 28), and a gated recurrent neural network (FIG. 29). The platformmay also be associated with further neural net systems such as an autoencoder neural network (FIG. 30), a variational neural network (FIG.31), a denoising neural network (FIG. 32), a sparse neural network (FIG.33), a Markov chain neural network (FIG. 34), and a Hopfield networkneural network (FIG. 35). The platform may further be associated withadditional neural net systems such as a Boltzmann machine neural network(FIG. 36), a restricted BM neural network (FIG. 37), a deep beliefneural network (FIG. 38), a deep convolutional neural network (FIG. 39),a deconvolutional neural network (FIG. 40), and a deep convolutionalinverse graphics neural network (FIG. 41). The platform may also beassociated with further neural net systems such as a generativeadversarial neural network (FIG. 42), a liquid state machine neuralnetwork (FIG. 43), an extreme learning machine neural network (FIG. 44),an echo state neural network (FIG. 45), a deep residual neural network(FIG. 46), a Kohonen neural network (FIG. 47), a support vector machineneural network (FIG. 48), and a neural Turing machine neural network(FIG. 49).

The foregoing neural networks may have a variety of nodes or neurons,which may perform a variety of functions on inputs, such as inputsreceived from sensors or other data sources, including other nodes.Functions may involve weights, features, feature vectors, and the like.Neurons may include perceptrons, neurons that mimic biological functions(such as of the human senses of touch, vision, taste, hearing, andsmell), and the like. Continuous neurons, such as with sigmoidalactivation, may be used in the context of various forms of neural net,such as where back propagation is involved.

In many embodiments, an expert system or neural network may be trained,such as by a human operator or supervisor, or based on a data set,model, or the like. Training may include presenting the neural networkwith one or more training data sets that represent values, such assensor data, event data, parameter data, and other types of data(including the many types described throughout this disclosure), as wellas one or more indicators of an outcome, such as an outcome of aprocess, an outcome of a calculation, an outcome of an event, an outcomeof an activity, or the like. Training may include training inoptimization, such as training a neural network to optimize one or moresystems based on one or more optimization approaches, such as Bayesianapproaches, parametric Bayes classifier approaches, k-nearest-neighborclassifier approaches, iterative approaches, interpolation approaches,Pareto optimization approaches, algorithmic approaches, and the like.Feedback may be provided in a process of variation and selection, suchas with a genetic algorithm that evolves one or more solutions based onfeedback through a series of rounds.

In embodiments, a plurality of neural networks may be deployed in acloud platform that receives data streams and other inputs collected(such as by mobile data collectors) in one or more transactionalenvironments and transmitted to the cloud platform over one or morenetworks, including using network coding to provide efficienttransmission. In the cloud platform, optionally using massively parallelcomputational capability, a plurality of different neural networks ofvarious types (including modular forms, structure-adaptive forms,hybrids, and the like) may be used to undertake prediction,classification, control functions, and provide other outputs asdescribed in connection with expert systems disclosed throughout thisdisclosure. The different neural networks may be structured to competewith each other (optionally including use evolutionary algorithms,genetic algorithms, or the like), such that an appropriate type ofneural network, with appropriate input sets, weights, node types andfunctions, and the like, may be selected, such as by an expert system,for a specific task involved in a given context, workflow, environmentprocess, system, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed forwardneural network, which moves information in one direction, such as from adata input, like a data source related to at least one resource orparameter related to a transactional environment, such as any of thedata sources mentioned throughout this disclosure, through a series ofneurons or nodes, to an output. Data may move from the input nodes tothe output nodes, optionally passing through one or more hidden nodes,without loops. In embodiments, feed forward neural networks may beconstructed with various types of units, such as binary McCulloch-Pittsneurons, the simplest of which is a perceptron.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a capsule neuralnetwork, such as for prediction, classification, or control functionswith respect to a transactional environment, such as relating to one ormore of the machines and automated systems described throughout thisdisclosure.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, which may be preferred in some situationsinvolving interpolation in a multi-dimensional space (such as whereinterpolation is helpful in optimizing a multi-dimensional function,such as for optimizing a data marketplace as described here, optimizingthe efficiency or output of a power generation system, a factory system,or the like, or other situation involving multiple dimensions. Inembodiments, each neuron in the RBF neural network stores an examplefrom a training set as a “prototype.” Linearity involved in thefunctioning of this neural network offers RBF the advantage of nottypically suffering from problems with local minima or maxima.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, such as one that employs a distancecriterion with respect to a center (e.g., a Gaussian function). A radialbasis function may be applied as a replacement for a hidden layer, suchas a sigmoidal hidden layer transfer, in a multi-layer perceptron. AnRBF network may have two layers, such as where an input is mapped ontoeach RBF in a hidden layer. In embodiments, an output layer may comprisea linear combination of hidden layer values representing, for example, amean predicted output. The output layer value may provide an output thatis the same as or similar to that of a regression model in statistics.In classification problems, the output layer may be a sigmoid functionof a linear combination of hidden layer values, representing a posteriorprobability. Performance in both cases is often improved by shrinkagetechniques, such as ridge regression in classical statistics. Thiscorresponds to a prior belief in small parameter values (and thereforesmooth output functions) in a Bayesian framework. RBF networks may avoidlocal minima, because the only parameters that are adjusted in thelearning process are the linear mapping from hidden layer to outputlayer. Linearity ensures that the error surface is quadratic andtherefore has a single minimum. In regression problems, this may befound in one matrix operation. In classification problems, the fixednon-linearity introduced by the sigmoid output function may be handledusing an iteratively re-weighted least squares function or the like. RBFnetworks may use kernel methods such as support vector machines (SVM)and Gaussian processes (where the RBF is the kernel function). Anon-linear kernel function may be used to project the input data into aspace where the learning problem may be solved using a linear model.

In embodiments, an RBF neural network may include an input layer, ahidden layer, and a summation layer. In the input layer, one neuronappears in the input layer for each predictor variable. In the case ofcategorical variables, N−1 neurons are used, where N is the number ofcategories. The input neurons may, in embodiments, standardize the valueranges by subtracting the median and dividing by the interquartilerange. The input neurons may then feed the values to each of the neuronsin the hidden layer. In the hidden layer, a variable number of neuronsmay be used (determined by the training process). Each neuron mayconsist of a radial basis function that is centered on a point with asmany dimensions as a number of predictor variables. The spread (e.g.,radius) of the RBF function may be different for each dimension. Thecenters and spreads may be determined by training. When presented withthe vector of input values from the input layer, a hidden neuron maycompute a Euclidean distance of the test case from the neuron's centerpoint and then apply the RBF kernel function to this distance, such asusing the spread values. The resulting value may then be passed to thesummation layer. In the summation layer, the value coming out of aneuron in the hidden layer may be multiplied by a weight associated withthe neuron and may add to the weighted values of other neurons. This sumbecomes the output. For classification problems, one output is produced(with a separate set of weights and summation units) for each targetcategory. The value output for a category is the probability that thecase being evaluated has that category. In training of an RBF, variousparameters may be determined, such as the number of neurons in a hiddenlayer, the coordinates of the center of each hidden-layer function, thespread of each function in each dimension, and the weights applied tooutputs as they pass to the summation layer. Training may be used byclustering algorithms (such as k-means clustering), by evolutionaryapproaches, and the like.

In embodiments, a recurrent neural network may have a time-varying,real-valued (more than just zero or one) activation (output). Eachconnection may have a modifiable real-valued weight. Some of the nodesare called labeled nodes, some output nodes, and others hidden nodes.For supervised learning in discrete time settings, training sequences ofreal-valued input vectors may become sequences of activations of theinput nodes, one input vector at a time. At each time step, eachnon-input unit may compute its current activation as a nonlinearfunction of the weighted sum of the activations of all units from whichit receives connections. The system may explicitly activate (independentof incoming signals) some output units at certain time steps.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingneural network, such as a Kohonen self-organizing neural network, suchas for visualization of views of data, such as low-dimensional views ofhigh-dimensional data. The self-organizing neural network may applycompetitive learning to a set of input data, such as from one or moresensors or other data inputs from or associated with a transactionalenvironment, including any machine or component that relates to thetransactional environment. In embodiments, the self-organizing neuralnetwork may be used to identify structures in data, such as unlabeleddata, such as in data sensed from a range of data sources about orsensors in or about in a transactional environment, where sources of thedata are unknown (such as where events may be coming from any of a rangeof unknown sources). The self-organizing neural network may organizestructures or patterns in the data, such that they may be recognized,analyzed, and labeled, such as identifying market behavior structures ascorresponding to other events and signals.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a recurrent neuralnetwork, which may allow for a bi-directional flow of data, such aswhere connected units (e.g., neurons or nodes) form a directed cycle.Such a network may be used to model or exhibit dynamic temporalbehavior, such as involved in dynamic systems, such as a wide variety ofthe automation systems, machines and devices described throughout thisdisclosure, such as an automated agent interacting with a marketplacefor purposes of collecting data, testing spot market transactions,execution transactions, and the like, where dynamic system behaviorinvolves complex interactions that a user may desire to understand,predict, control and/or optimize. For example, the recurrent neuralnetwork may be used to anticipate the state of a market, such as oneinvolving a dynamic process or action, such as a change in state of aresource that is traded in or that enables a marketplace oftransactional environment. In embodiments, the recurrent neural networkmay use internal memory to process a sequence of inputs, such as fromother nodes and/or from sensors and other data inputs from or about thetransactional environment, of the various types described herein. Inembodiments, the recurrent neural network may also be used for patternrecognition, such as for recognizing a machine, component, agent, orother item based on a behavioral signature, a profile, a set of featurevectors (such as in an audio file or image), or the like. In anon-limiting example, a recurrent neural network may recognize a shiftin an operational mode of a marketplace or machine by learning toclassify the shift from a training data set consisting of a stream ofdata from one or more data sources of sensors applied to or about one ormore resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a modular neuralnetwork, which may comprise a series of independent neural networks(such as ones of various types described herein) that are moderated byan intermediary. Each of the independent neural networks in the modularneural network may work with separate inputs, accomplishing subtasksthat make up the task the modular network as whole is intended toperform. For example, a modular neural network may comprise a recurrentneural network for pattern recognition, such as to recognize what typeof machine or system is being sensed by one or more sensors that areprovided as input channels to the modular network and an RBF neuralnetwork for optimizing the behavior of the machine or system onceunderstood. The intermediary may accept inputs of each of the individualneural networks, process them, and create output for the modular neuralnetwork, such an appropriate control parameter, a prediction of state,or the like.

Combinations among any of the pairs, triplets, or larger combinations,of the various neural network types described herein, are encompassed bythe present disclosure. This may include combinations where an expertsystem uses one neural network for recognizing a pattern (e.g., apattern indicating a problem or fault condition) and a different neuralnetwork for self-organizing an activity or workflow based on therecognized pattern (such as providing an output governing autonomouscontrol of a system in response to the recognized condition or pattern).This may also include combinations where an expert system uses oneneural network for classifying an item (e.g., identifying a machine, acomponent, or an operational mode) and a different neural network forpredicting a state of the item (e.g., a fault state, an operationalstate, an anticipated state, a maintenance state, or the like). Modularneural networks may also include situations where an expert system usesone neural network for determining a state or context (such as a stateof a machine, a process, a work flow, a marketplace, a storage system, anetwork, a data collector, or the like) and a different neural networkfor self-organizing a process involving the state or context (e.g., adata storage process, a network coding process, a network selectionprocess, a data marketplace process, a power generation process, amanufacturing process, a refining process, a digging process, a boringprocess, or other process described herein).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a physical neuralnetwork where one or more hardware elements is used to perform orsimulate neural behavior. In embodiments, one or more hardware neuronsmay be configured to stream voltage values, current values, or the likethat represent sensor data, such as to calculate information from analogsensor inputs representing energy consumption, energy production, or thelike, such as by one or more machines providing energy or consumingenergy for one or more transactions. One or more hardware nodes may beconfigured to stream output data resulting from the activity of theneural net. Hardware nodes, which may comprise one or more chips,microprocessors, integrated circuits, programmable logic controllers,application-specific integrated circuits, field-programmable gatearrays, or the like, may be provided to optimize the machine that isproducing or consuming energy, or to optimize another parameter of somepart of a neural net of any of the types described herein. Hardwarenodes may include hardware for acceleration of calculations (such asdedicated processors for performing basic or more sophisticatedcalculations on input data to provide outputs, dedicated processors forfiltering or compressing data, dedicated processors for de-compressingdata, dedicated processors for compression of specific file or datatypes (e.g., for handling image data, video streams, acoustic signals,thermal images, heat maps, or the like), and the like. A physical neuralnetwork may be embodied in a data collector, including one that may bereconfigured by switching or routing inputs in varying configurations,such as to provide different neural net configurations within the datacollector for handling different types of inputs (with the switching andconfiguration optionally under control of an expert system, which mayinclude a software-based neural net located on the data collector orremotely). A physical, or at least partially physical, neural networkmay include physical hardware nodes located in a storage system, such asfor storing data within a machine, a data storage system, a distributedledger, a mobile device, a server, a cloud resource, or in atransactional environment, such as for accelerating input/outputfunctions to one or more storage elements that supply data to or takedata from the neural net. A physical, or at least partially physical,neural network may include physical hardware nodes located in a network,such as for transmitting data within, to or from an industrialenvironment, such as for accelerating input/output functions to one ormore network nodes in the net, accelerating relay functions, or thelike. In embodiments of a physical neural network, an electricallyadjustable resistance material may be used for emulating the function ofa neural synapse. In embodiments, the physical hardware emulates theneurons, and software emulates the neural network between the neurons.In embodiments, neural networks complement conventional algorithmiccomputers. They are versatile and may be trained to perform appropriatefunctions without the need for any instructions, such as classificationfunctions, optimization functions, pattern recognition functions,control functions, selection functions, evolution functions, and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a multilayeredfeed forward neural network, such as for complex pattern classificationof one or more items, phenomena, modes, states, or the like. Inembodiments, a multilayered feed forward neural network may be trainedby an optimization technique, such as a genetic algorithm, such as toexplore a large and complex space of options to find an optimum, ornear-optimum, global solution. For example, one or more geneticalgorithms may be used to train a multilayered feed forward neuralnetwork to classify complex phenomena, such as to recognize complexoperational modes of machines, such as modes involving complexinteractions among machines (including interference effects, resonanceeffects, and the like), modes involving non-linear phenomena, modesinvolving critical faults, such as where multiple, simultaneous faultsoccur, making root cause analysis difficult, and others. In embodiments,a multilayered feed forward neural network may be used to classifyresults from monitoring of a marketplace, such as monitoring systems,such as automated agents, that operate within the marketplace, as wellas monitoring resources that enable the marketplace, such as computing,networking, energy, data storage, energy storage, and other resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed-forward,back-propagation multi-layer perceptron (MLP) neural network, such asfor handling one or more remote sensing applications, such as for takinginputs from sensors distributed throughout various transactionalenvironments. In embodiments, the MLP neural network may be used forclassification of transactional environments and resource environments,such as spot markets, forward markets, energy markets, renewable energycredit (REC) markets, networking markets, advertising markets, spectrummarkets, ticketing markets, rewards markets, compute markets, and othersmentioned throughout this disclosure, as well as physical resources andenvironments that produce them, such as energy resources (includingrenewable energy environments, mining environments, explorationenvironments, drilling environments, and the like, includingclassification of geological structures (including underground featuresand above ground features), classification of materials (includingfluids, minerals, metals, and the like), and other problems. This mayinclude fuzzy classification.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use astructure-adaptive neural network, where the structure of a neuralnetwork is adapted, such as based on a rule, a sensed condition, acontextual parameter, or the like. For example, if a neural network doesnot converge on a solution, such as classifying an item or arriving at aprediction, when acting on a set of inputs after some amount oftraining, the neural network may be modified, such as from a feedforward neural network to a recurrent neural network, such as byswitching data paths between some subset of nodes from unidirectional tobi-directional data paths. The structure adaptation may occur undercontrol of an expert system, such as to trigger adaptation uponoccurrence of a trigger, rule or event, such as recognizing occurrenceof a threshold (such as an absence of a convergence to a solution withina given amount of time) or recognizing a phenomenon as requiringdifferent or additional structure (such as recognizing that a system isvarying dynamically or in a non-linear fashion). In one non-limitingexample, an expert system may switch from a simple neural networkstructure like a feed forward neural network to a more complex neuralnetwork structure like a recurrent neural network, a convolutionalneural network, or the like upon receiving an indication that acontinuously variable transmission is being used to drive a generator,turbine, or the like in a system being analyzed.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an autoencoder,autoassociator or Diabolo neural network, which may be similar to amultilayer perceptron (MLP) neural network, such as where there may bean input layer, an output layer and one or more hidden layers connectingthem. However, the output layer in the auto-encoder may have the samenumber of units as the input layer, where the purpose of the MLP neuralnetwork is to reconstruct its own inputs (rather than just emitting atarget value). Therefore, the auto encoders may operate as anunsupervised learning model. An auto encoder may be used, for example,for unsupervised learning of efficient codings, such as fordimensionality reduction, for learning generative models of data, andthe like. In embodiments, an auto-encoding neural network may be used toself-learn an efficient network coding for transmission of analog sensordata from a machine over one or more networks or of digital data fromone or more data sources. In embodiments, an auto-encoding neuralnetwork may be used to self-learn an efficient storage approach forstorage of streams of data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a probabilisticneural network (PNN), which, in embodiments, may comprise a multi-layer(e.g., four-layer) feed forward neural network, where layers may includeinput layers, hidden layers, pattern/summation layers and an outputlayer. In an embodiment of a PNN algorithm, a parent probabilitydistribution function (PDF) of each class may be approximated, such asby a Parzen window and/or a non-parametric function. Then, using the PDFof each class, the class probability of a new input is estimated, andBayes' rule may be employed, such as to allocate it to the class withthe highest posterior probability. A PNN may embody a Bayesian networkand may use a statistical algorithm or analytic technique, such asKernel Fisher discriminant analysis technique. The PNN may be used forclassification and pattern recognition in any of a wide range ofembodiments disclosed herein. In one non-limiting example, aprobabilistic neural network may be used to predict a fault condition ofan engine based on collection of data inputs from sensors andinstruments for the engine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a time delayneural network (TDNN), which may comprise a feed forward architecturefor sequential data that recognizes features independent of sequenceposition. In embodiments, to account for time shifts in data, delays areadded to one or more inputs, or between one or more nodes, so thatmultiple data points (from distinct points in time) are analyzedtogether. A time delay neural network may form part of a larger patternrecognition system, such as using a perceptron network. In embodiments,a TDNN may be trained with supervised learning, such as where connectionweights are trained with back propagation or under feedback. Inembodiments, a TDNN may be used to process sensor data from distinctstreams, such as a stream of velocity data, a stream of accelerationdata, a stream of temperature data, a stream of pressure data, and thelike, where time delays are used to align the data streams in time, suchas to help understand patterns that involve understanding of the variousstreams (e.g., changes in price patterns in spot or forward markets).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a convolutionalneural network (referred to in some cases as a CNN, a ConvNet, a shiftinvariant neural network, or a space invariant neural network), whereinthe units are connected in a pattern similar to the visual cortex of thehuman brain. Neurons may respond to stimuli in a restricted region ofspace, referred to as a receptive field. Receptive fields may partiallyoverlap, such that they collectively cover the entire (e.g., visual)field. Node responses may be calculated mathematically, such as by aconvolution operation, such as using multilayer perceptrons that useminimal preprocessing. A convolutional neural network may be used forrecognition within images and video streams, such as for recognizing atype of machine in a large environment using a camera system disposed ona mobile data collector, such as on a drone or mobile robot. Inembodiments, a convolutional neural network may be used to provide arecommendation based on data inputs, including sensor inputs and othercontextual information, such as recommending a route for a mobile datacollector. In embodiments, a convolutional neural network may be usedfor processing inputs, such as for natural language processing ofinstructions provided by one or more parties involved in a workflow inan environment. In embodiments, a convolutional neural network may bedeployed with a large number of neurons (e.g., 100,000, 500,000 ormore), with multiple (e.g., 4, 5, 6 or more) layers, and with many(e.g., millions) of parameters. A convolutional neural net may use oneor more convolutional nets.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a regulatoryfeedback network, such as for recognizing emergent phenomena (such asnew types of behavior not previously understood in a transactionalenvironment).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingmap (SOM), involving unsupervised learning. A set of neurons may learnto map points in an input space to coordinates in an output space. Theinput space may have different dimensions and topology from the outputspace, and the SOM may preserve these while mapping phenomena intogroups.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a learning vectorquantization neural net (LVQ). Prototypical representatives of theclasses may parameterize, together with an appropriate distance measure,in a distance-based classification scheme.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an echo statenetwork (ESN), which may comprise a recurrent neural network with asparsely connected, random hidden layer. The weights of output neuronsmay be changed (e.g., the weights may be trained based on feedback). Inembodiments, an ESN may be used to handle time series patterns, such as,in an example, recognizing a pattern of events associated with a market,such as the pattern of price changes in response to stimuli.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a Bi-directional,recurrent neural network (BRNN), such as using a finite sequence ofvalues (e.g., voltage values from a sensor) to predict or label eachelement of the sequence based on both the past and the future context ofthe element. This may be done by adding the outputs of two RNNs, such asone processing the sequence from left to right, the other one from rightto left. The combined outputs are the predictions of target signals,such as ones provided by a teacher or supervisor. A bi-directional RNNmay be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchical RNNthat connects elements in various ways to decompose hierarchicalbehavior, such as into useful subprograms. In embodiments, ahierarchical RNN may be used to manage one or more hierarchicaltemplates for data collection in a transactional environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a stochasticneural network, which may introduce random variations into the network.Such random variations may be viewed as a form of statistical sampling,such as Monte Carlo sampling.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a genetic scalerecurrent neural network. In such embodiments, an RNN (often an LSTM) isused where a series is decomposed into a number of scales where everyscale informs the primary length between two consecutive points. A firstorder scale consists of a normal RNN, a second order consists of allpoints separated by two indices and so on. The Nth order RNN connectsthe first and last node. The outputs from all the various scales may betreated as a committee of members, and the associated scores may be usedgenetically for the next iteration.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a committee ofmachines (CoM), comprising a collection of different neural networksthat together “vote” on a given example. Because neural networks maysuffer from local minima, starting with the same architecture andtraining, but using randomly different initial weights often givesdifferent results. A CoM tends to stabilize the result.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an associativeneural network (ASNN), such as involving an extension of a committee ofmachines that combines multiple feed forward neural networks and ak-nearest neighbor technique. It may use the correlation betweenensemble responses as a measure of distance amid the analyzed cases forthe kNN. This corrects the bias of the neural network ensemble. Anassociative neural network may have a memory that may coincide with atraining set. If new data become available, the network instantlyimproves its predictive ability and provides data approximation(self-learns) without retraining. Another important feature of ASNN isthe possibility to interpret neural network results by analysis ofcorrelations between data cases in the space of models.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an instantaneouslytrained neural network (ITNN), where the weights of the hidden and theoutput layers are mapped directly from training vector data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a spiking neuralnetwork, which may explicitly consider the timing of inputs. The networkinput and output may be represented as a series of spikes (such as adelta function or more complex shapes). SNNs may process information inthe time domain (e.g., signals that vary over time, such as signalsinvolving dynamic behavior of markets or transactional environments).They are often implemented as recurrent networks.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a dynamic neuralnetwork that addresses nonlinear multivariate behavior and includeslearning of time-dependent behavior, such as transient phenomena anddelay effects. Transients may include behavior of shifting marketvariables, such as prices, available quantities, availablecounterparties, and the like.

In embodiments, cascade correlation may be used as an architecture andsupervised learning algorithm, supplementing adjustment of the weightsin a network of fixed topology. Cascade-correlation may begin with aminimal network, then automatically trains and add new hidden units oneby one, creating a multi-layer structure. Once a new hidden unit hasbeen added to the network, its input-side weights may be frozen. Thisunit then becomes a permanent feature-detector in the network, availablefor producing outputs or for creating other, more complex featuredetectors. The cascade-correlation architecture may learn quickly,determine its own size and topology, and retain the structures it hasbuilt even if the training set changes and requires no back-propagation.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a neuro-fuzzynetwork, such as involving a fuzzy inference system in the body of anartificial neural network. Depending on the type, several layers maysimulate the processes involved in a fuzzy inference, such asfuzzification, inference, aggregation and defuzzification. Embedding afuzzy system in a general structure of a neural net as the benefit ofusing available training methods to find the parameters of a fuzzysystem.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a compositionalpattern-producing network (CPPN), such as a variation of an associativeneural network (ANN) that differs the set of activation functions andhow they are applied. While typical ANNs often contain only sigmoidfunctions (and sometimes Gaussian functions), CPPNs may include bothtypes of functions and many others. Furthermore, CPPNs may be appliedacross the entire space of possible inputs, so that they may represent acomplete image. Since they are compositions of functions, CPPNs ineffect encode images at infinite resolution and may be sampled for aparticular display at whatever resolution is optimal.

This type of network may add new patterns without re-training. Inembodiments, methods and systems described herein that involve an expertsystem or self-organization capability may use a one-shot associativememory network, such as by creating a specific memory structure, whichassigns each new pattern to an orthogonal plane using adjacentlyconnected hierarchical arrays.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchicaltemporal memory (HTM) neural network, such as involving the structuraland algorithmic properties of the neocortex. HTM may use a biomimeticmodel based on memory-prediction theory. HTM may be used to discover andinfer the high-level causes of observed input patterns and sequences.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a holographicassociative memory (HAM) neural network, which may comprise an analog,correlation-based, associative, stimulus-response system. Informationmay be mapped onto the phase orientation of complex numbers. The memoryis effective for associative memory tasks, generalization and patternrecognition with changeable attention.

In embodiments, various embodiments involving network coding may be usedto code transmission data among network nodes in a neural net, such aswhere nodes are located in one or more data collectors or machines in atransactional environment.

Referring to FIG. 50, a system 5000 for automated loan management isdepicted. A variety of entities/parties 5038 may have a connection to aloan 5024 including a borrower 5040, a lender 5042, 3rd parties 5044such as a neutral 3rd party (e.g. such as an assessor, acollateral/equipment 5060, or an interested 3rd party (e.g. a regulator,company employees, and the like). A loan 5024 may be subject to a smartlending contract 5090 including information such as loan terms andconditions 5029, loan actions 5030, loan events 5032, lender priorities5028. And the like. The smart lending contract 5090 may be recording inloan entry 5041 in a distributed ledger 5063. The smart lending contract5090 may be stored as blockchain data 5034.

In an illustrative example, controller 5022 may receive collateral data5074 such as collateral related events 5008, collateral attributes 5010,environmental data 5012 about an environment in which the collateral5002 is situated, sensor data 5014 where the senor 5004 may be affixedto an item of collateral, to a case containing an item of collateral orin proximity to an item of collateral. In embodiments, collateral datamay be acquired by an Internet of Things Circuit 5020, a camera system,a networked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

The controller 5022 may also monitor and/or receive data from a socialnetwork information 5058 from which a financial condition 5092 may beinferred such as a rating of a party, a tax status of a party, a creditreport of the party, a credit rating of a party, a website rating of aparty, a set of customer reviews for a product of a party, a socialnetwork rating of a party, a set of credentials of a party, a set ofreferrals of a party, a set of testimonials for a party, a set ofbehavior of a party, and the like. The controller 5022 may also receivemarketplace information 5048 such as pricing 5050, financial data 5054such as a publicly stated valuation of the party, a set of propertyowned by the party as indicated by public records, a valuation of a setof property owned by the party, a bankruptcy condition of the party, aforeclosure status of the entity, a contractual default status of theentity, a regulatory violation status of the entity, a criminal statusof the entity, an export controls status of the entity, an embargostatus of the entity, a tariff status of the entity, a tax status of theentity, a credit report of the entity, a credit rating of the entity,and the like.

In embodiments, artificial intelligence systems 5062 may be part of acontroller 5022 or on remote systems. The AI systems 5062 may include avaluation circuit 5064 structured to determine a value for an item ofcollateral based on collateral data 5074 and a valuation model and avalue model improvement circuit 5066 to improve the valuation model onthe basis of a first set of received collateral data 5074 and theoutcome of loans for which collateral associated with that first set ofreceived collateral data acted as security. The AI systems 5062 mayinclude an automated agent circuit 5070 that takes action based oncollateral events, loan-events and the like. Actions may includeloan-related actions such as offering the loan, accepting the loan,underwriting the loan, setting an interest rate for the loan, deferringa payment requirement, modifying an interest rate for the loan,validating title for collateral, recording a change in title, assessinga value of collateral, initiating inspection of collateral, calling theloan, closing the loan, setting terms and conditions for the loan,providing notices required to be provided to a borrower, foreclosing onproperty subject to the loan, modifying terms and conditions for theloan, and the like. Actions may include collateral-related actions suchas validating title for the one of the assigned set of items ofcollateral, recording a change in title for the one of the assigned setof items of collateral, assessing the value of the one of the assignedset of items of collateral, initiating inspection of the one of theassigned set of items of collateral, initiating maintenance of the oneof the assigned set of items of collateral, initiating security for theone of the assigned set of items of collateral, modifying terms andconditions for the one of the assigned set of items of collateral 5018,and the like. The AI systems 5062 may include a cluster circuit 5072 tocreate groups of items of collateral based on a common attribute. Thecluster circuit 5072 may also determine a group of offset items ofcollateral where the offset items of collateral share a common attributewith one or more items of collateral. Data may be gathered on the offsetitems of collateral and use it as representative of the items ofcollateral. A smart contract circuit 5068 may create a smart lendingcontract 5090 as described elsewhere herein.

Referring to FIG. 51, a controller may include a blockchain servicecircuit 5144 structured to interpret a plurality of access controlfeatures 5148 such as corresponding to parties associated with a loan5130 and associated with blockchain data 5140. The system 5100 mayinclude a data collection circuit 5112 structured to interpret entityinformation 5102, collateral data 5104, and the like, such ascorresponding to entities related to a lending transaction correspondingto the loan, collateral conditions, and the like. The system may includea smart contract circuit 5122 structured to specify loan terms andconditions 5124, contracts 5128, and the like, relating to the loan. Thesystem may include a loan management circuit 5132 structured tointerpret loan related actions 5134 and/or events 5138 in response tothe entity information, the plurality of access control features, andthe loan terms and conditions, where the loan related events areassociated with the loan; implement loan related activities in responseto the entity information, the plurality of access control features, andthe loan terms and conditions, wherein the loan related activities areassociated with the loan; and where each of the blockchain servicecircuit, the data collection circuit, the smart contract circuit, andthe loan management circuit further comprise a corresponding applicationprogramming interface (API) component structured to facilitatecommunication among the circuits of the system. For example, a lender5108 may interface with the controller through secure access controlinterface 5152 (e.g., through access control instructions 5154)structured to interface to the controller through a secure accesscontrol circuit 5150. The data collection circuit 5112 may be structuredto receive collateral data 5104 and entity information 5102 such asinformation about parties to the loan such as a lender, a borrower, or athird party, an item of collateral, a machine or property associatedwith a party to the loan, a product of a party to the loan, and thelike. Collateral data 5104 may include a type of the item of collateral,a category of the item of collateral, a value of the item of collateral,a price of a type of the item of collateral, a value of a type of theitem of collateral, a specification of the item of collateral, a productfeature set of the item of collateral, a model of the item ofcollateral, a brand of the item of collateral, a manufacturer of theitem of collateral, an age of the item of collateral, a liquidity of theitem of collateral, a shelf-life of the item of collateral, a usefullife of the item of collateral, a condition of the item of collateral, avaluation of the item of collateral, a status of the item of collateral,a context of the item of collateral, a state of the item of collateral,a storage location of the item of collateral, a history of the item ofcollateral, an ownership of the item of collateral, a caretaker of theitem of collateral, a security of the item of collateral, a condition ofan owner of the item of collateral, a lien on the item of collateral, astorage condition of the item of collateral, a maintenance history ofthe item of collateral, a usage history of the item of collateral, anaccident history of the item of collateral, a fault history of the itemof collateral, a history of ownership of the item of collateral, anassessment of the item of collateral, a geolocation of the item ofcollateral, a jurisdictional location of the item of collateral, and thelike. The data collection circuit 5112 may determine a collateralcondition based on the received data. The received data 5102, 5104 andthe collateral condition 5110 may be provided to AI circuits 5142 whichmay include an automated agent circuit 5114 (e.g., processing events5118, 5120), a smart contract services circuit 5122 and a loanmanagement circuit 5132.

Referring to FIG. 52, an illustrative and non-limiting example methodfor handling a loan 5200 is depicted. The example method may includeinterpreting a plurality of access control features (step 5202);interpreting entity information (step 5204); specifying loan terms andconditions (step 5208); performing a contract related events in responseto entity information (step 5210); interpreting an event relevant to theloan (step 5212); performing a loan action in response to the event(step 5214); providing a user interface (step 5218); creating a smartlending contract (step 5220); and recording the smart lending contractas blockchain data (step 5222).

Referring to FIG. 53, depicts a system 5300 for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. The system 5300 may include acontroller 5323 which may include a data collection circuit 5302 whichreceives collateral data 5301 and determines collateral condition 5304.The controller 5323 may further include a plurality of AI circuits 5654.The plurality of AI circuits 5654 may include a valuation circuit 5308which may include a valuation model improvement circuit 5310 and acluster circuit 5312. The plurality of AI circuits 5654 may include asmart contract services circuit 5314 including smart lending contracts5316 for loans 5325. The plurality of AI circuits 5654 may include anautomated agent circuit 5318 which takes loan-related actions 5320. Thecontroller 5323 may further include a reporting circuit 5322 and amarket value monitoring circuit 5324 which also determines collateralcondition 5304. The controller 5323 may further include a secure accessuser interface 5328 which receives access control instructions 5330 fromlenders 5342. The access control instructions 5330 are provided to asecure access control circuit 5332 which provides instructions toblockchain service circuit 5334 which interprets access control features5338 and provides access to a lender 5342 or other party. The blockchainservice circuit 5334 all stores the collateral data and a uniquecollateral ID as blockchain data 5335.

Referring to FIG. 54, a method 5400 for automated smart contractcreation and collateral assignment is depicted. The method 5400 mayinclude receiving first and second collateral data regarding an item ofcollateral 5402, creating a smart lending contract 5404, associating thecollateral data with a unique identifier for the item of collateral5408, and storing the unique identifier and the collateral in ablockchain structure 5410. The method may further include interpreting acondition of the collateral based on the collateral data 5412,identifying a collateral event 5414, reporting a collateral event 5418,and performing an action in response to the collateral 5420. The method5400 may further include identifying a group of offset items ofcollateral 5422, accessing marketplace information relevant to theoffset items of collateral or the item of collateral 5424, and modifyinga term or condition of the loan based on the marketplace information5428. The method 5400 may further include receiving access controlinstructions 5430, interpreting a plurality of access control features5432, and providing access to the collateral date 5434.

Referring to FIG. 55, an illustrative and non-limiting example system5500 for handling a loan 5530 is depicted. The example system mayinclude a controller 5501. The controller 5501 may include a datacollection circuit 5512, a valuation circuit 5544, a user interface 5554(e.g., for interface with a user 5506), a blockchain service circuit5558, and several artificial intelligence circuits 5542 including asmart contract services circuit 5522, a loan management circuit 5922, aclustering circuit 5532, an automated agent circuit 5514 (e.g., forprocessing loan related events 5539 and loan actions 5538).

The blockchain service circuit 5558 may be structured to interface witha distributed ledger 5540. The data collection circuit 5512 may bestructured to receive data related to a plurality of items of collateral5504 or data related to environments of the plurality of items ofcollateral 5502. The valuation circuit 5544 may be structured todetermine a value for each of the plurality of items of collateral basedon a valuation model 5552 and the received data. The smart contractservices circuit 5522 may be structured to interpret a smart lendingcontract 5531 for a loan, and to modify the smart lending contract 5531by assigning, based on the determined value for each of the plurality ofitems of collateral, at least a portion of the plurality of items ofcollateral 5528 as security for the loan such that the determined valueof the of the plurality of items of collateral is sufficient to providesecurity for the loan. The blockchain service circuit 5558 may befurther structured to record the assigned at least a portion of items ofcollateral 5528 to an entry in the distributed ledger 5540, wherein theentry is used to record events relevant to the loan. Each of theblockchain service circuit, the data collection circuit, the valuationcircuit and the smart contract circuit may further include acorresponding application programming interface (API) componentstructured to facilitate communication among the circuits of the system.

Modifying the smart lending contract 5531 may further include specifyingterms and conditions 5524 that govern an item selected from the listconsisting of: a loan term, a loan condition, a loan-related event, anda loan-related activity. The terms and conditions 5524 may each includeat least one member selected from the group consisting of: a principalamount of the loan, a balance of the loan, a fixed interest rate, avariable interest rate description, a payment amount, a paymentschedule, a balloon payment schedule, a collateral specification, acollateral substitution description, a description of at least one ofthe parties, a guarantee description, a guarantor description, asecurity description, a personal guarantee, a lien, a foreclosurecondition, a default condition, a consequence of default, a covenantrelated to any one of the foregoing, and a duration of any one of theforegoing.

The loan 5530 may include at least one loan type selected from the loantypes consisting of: an auto loan, an inventory loan, a capitalequipment loan, a bond for performance, a capital improvement loan, abuilding loan, a loan backed by an account receivable, an invoicefinance arrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The item of collateral may include at least one item selected from theitems consisting of: a vehicle, a ship, a plane, a building, a home, areal estate property, an undeveloped land property, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, a tool, an item of machinery, and an item ofpersonal property.

The data collection circuit 5512 may be further structured to receiveoutcome data 5510 related to the loan 5530 and a corresponding item ofcollateral, and wherein the valuation circuit 5544 comprises anartificial intelligent circuit structured to iteratively improve 5550the valuation model 5552 based on the outcome data 5510.

The valuation circuit 5544 may further include a market value datacollection circuit 5548 structured to monitor and report marketplaceinformation relevant to the value of at least one of the plurality ofitems of collateral. The market value data collection circuit 5548 maybe further structured to monitor pricing or financial data for itemsthat are similar to the item of collateral in at least one publicmarketplace.

The clustering circuit 5532 may be structured to identify a set ofoffset items 5534 for use in valuing the item of collateral based onsimilarity to an attribute of the collateral.

The attribute of the collateral may be selected from among a list ofattributes consisting of a category of the collateral, an age of thecollateral, a condition of the collateral, a history of the collateral,a storage condition of the collateral, and a geolocation of thecollateral.

The data collection circuit 5512 may be further structured to interpreta condition 5511 of the item of collateral.

The data collection circuit may further include at least one systemselected from the systems consisting of: an Internet of Things system, acamera system, a networked monitoring system, an internet monitoringsystem, a mobile device system, a wearable device system, a userinterface system, and an interactive crowdsourcing system.

The loan includes at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

A loan management circuit 5922 may be structured to interpret an eventrelevant to the loan 5539, and to perform an action 5538 related to theloan in response to the event relevant to the loan.

The event relevant to the loan may include an event relevant to at leastone of: a value of the loan, a condition of collateral of the loan, oran ownership of collateral of the loan.

The action related to the loan may include at least one of: modifyingthe terms and conditions for the loan, providing a notice to one of theparties, providing a required notice to a borrower of the loan, andforeclosing on a property subject to the loan.

The corresponding API components of the circuits may further includeuser interfaces structured to interact with a plurality of users of thesystem.

The plurality of users may each include: one of the plurality ofparties, one of the plurality of entities, or a representative of anyone of the foregoing. At least one of the plurality of users mayinclude: a prospective party, a prospective entity, or a representativeof any one of the foregoing.

Referring to FIG. 56, an illustrative and non-limiting example methodfor handling a loan 5600 is depicted. The example method may includereceiving data related to a plurality of items of collateral (step5602); setting a value for each of the plurality of items of collateral(step 5604); assigning at least a portion of the plurality of items ofcollateral as security for a loan (step 5608); and recording theassigned at least a portion of the plurality of items of collateral toan entry in a distributed ledger, wherein the entry is used to recordevents relevant to the loan (step 5610). A smart lending contract may bemodified for the loan (step 5612).

Terms and conditions may be specified for the loan (step 5614). Theterms and conditions are each selected from the list consisting of: aprincipal amount of debt, a balance of debt, a fixed interest rate, avariable interest rate, a payment amount, a payment schedule, a balloonpayment schedule, a party, a guarantee, a guarantor, a security, apersonal guarantee, a lien, a duration, a covenant, a foreclosecondition, a default condition, and a consequence of default.

Outcome data related to the loan may be received (step 5618). Avaluation model may be iteratively improved based on the outcome dataand corresponding collateral (step 5620). Marketplace informationrelevant to the value of at least one of the plurality of items ofcollateral may be monitored (step 5622).

A set of items similar to one of the plurality of items of collateralmay be identified based on similarity to an attribute of the one of theplurality of items of collateral (step 5624).

A condition of the one of the plurality of items of collateral may beinterpreted (step 5628).

Events related to a value of the one of the plurality of items ofcollateral, a condition of the one of the plurality of items ofcollateral, or an ownership of the one of the items of collateral may bereported (step 5630).

An event relevant to: a value of one of the plurality of items ofcollateral, a condition of one of the plurality of items of collateral,or an ownership of one of the plurality of items of collateral may beinterpreted (step 5632); and an action related to the secured loan inresponse to the event relevant to the one of the plurality of items ofcollateral for said secured loan may be performed (step 5634).

The loan-related action may be selected from among the actionsconsisting of: offering a loan, accepting a loan, underwriting a loan,setting an interest rate for a loan, deferring a payment requirement,modifying an interest rate for a loan, validating title for collateral,recording a change in title, assessing the value of collateral,initiating inspection of collateral, calling a loan, closing a loan,setting terms and conditions for a loan, providing notices required tobe provided to a borrower, foreclosing on property subject to a loan,and modifying terms and conditions for a loan.

Referring to FIG. 57, an illustrative and non-limiting example systemfor system for adaptive intelligence and robotic process automationcapabilities 5700 is depicted. The example system may include acontroller 5701. The controller may include a data collection circuit5728 which may collect data such as collateral data 5732, environmentaldata 5734 related to the collateral, and the like from a variety ofsources and systems such as: an Internet of Things system, a camerasystem, a networked monitoring system, an internet monitoring system, amobile device system, a wearable device system, a user interface system,and an interactive crowdsourcing system. Based on the received data5732, 5734 the data collection circuit 5728 may identify a collateralevent 5730.

The controller 5701 may also include a variety of AI circuits 5744,including a valuation circuit 5702 which may, based in part on thereceived data 5732, 5734, determine a value for an item of collateral.The valuation circuit 5702 may include a market value monitoring circuit5706 structured to determine market data regarding an item of collateralor an offset item of collateral, where the market data may contribute tothe valuation for the item of collateral. The AI circuits may alsoinclude a smart contract services circuit 5710 to facilitate servicesrelated to a loan 5729 such as creating a smart contract 5722,identifying terms and conditions 5724 for the smart contract 5722,identifying lender priorities and tracking apportionment of value 5726among lenders. The smart contract services circuit 5710 may provide datato a block chain service circuit 5736 which is able to create and modifya loan entry 5727 on a distributed ledger 5725 where the loan entry 5727may include terms and conditions, data regarding items of collateralused to secure the loan, lender priority and apportionment of value andthe like. The AI circuits 5744 may also include a collateralclassification circuit 5740 which creates groups of offset items ofcollateral 5704 which share at least one attribute with one of the itemsof collateral, where the common attribute may be a category of theitems, an age of the items, a condition of the items, a history of theitems, an ownership of the items, a caretaker of the items, a securityof the items, a condition of an owner of the items, a lien on the items,a storage condition of the items, a geolocation of the items, ajurisdictional location of the items, and the like. The use of offsetitems of collateral 5742 may facilitate the market value monitoringcircuit 5706 in obtaining relevant market data and in the overalldetermination of value for an item of collateral.

The data collection circuit 5728 may utilize the received data and adetermination of value for an item of collateral to identify acollateral event 5730. Based on the collateral event 5730, an automatedagent circuit 5746, may take an action 5748. The action 5748 may be aloan-related action such as offering the loan, accepting the loan,underwriting the loan, setting an interest rate for a loan, deferring apayment requirement, modifying the interest rate for the loan, callingthe loan, closing the loan, setting terms and conditions for the loan,providing notices required to be provided to a borrower, foreclosing onproperty subject to the loan, modifying terms and conditions for theloan, and the like. The action 5748 may be a collateral-related actionsuch as validating title for the one of a set of items of collateral,recording a change in title for one of a set of items of collateral,assessing the value of the one of a set of items of collateral,initiating inspection of one of a set of items of collateral, initiatingmaintenance of one of a set of items of collateral, initiating securityfor one of a set of items of collateral, modifying terms and conditionsfor one of a set of items of collateral, and the like.

Referring to FIG. 58, an illustrative and non-limiting example method5800 for loan creation and management is depicted. The example method5800 may include receiving data related to a set of items of collateral(step 5802) that provide security for a loan and receiving data relatedto an environment of one of a set of items of collateral (step 5804). Asmart lending contract for the loan may be created (step 5806) and theset of items of collateral may be recorded in the smart lending contract(step 5808). A loan-entry may be recoded in a distributed ledger (step5810) where the loan entry includes the smart lending contract or areference to the smart contract.

The value for each of the set of items of collateral may be determined(5812) and the value of the items of collateral may be apportioned amonglenders (step 5816) based on the priority of the different lenders. Thevaluation model may be modified (step 5814) based on a learning setincluding a set of valuation determinations of a set of items ofcollateral and the outcomes of loans having those items of collateral assecurity and the valuation of those items of collateral.

A collateral event may be determined (step 5818) based on received dataor a valuation of one of the items of collateral. A loan-related actionmay be performed in response to the determined collateral event (step5820) where the loan-related action includes offering the loan,accepting the loan, underwriting the loan, setting an interest rate fora loan, deferring a payment requirement, modifying the interest rate forthe loan, calling the loan, closing the loan, setting terms andconditions for the loan, providing notices required to be provided to aborrower, foreclosing on property subject to the loan, modifying termsand conditions for the loan, or the like.

A collateral-related action may be performed in response to thedetermined collateral event (step 5822), where the collateral-relatedaction includes validating title for the one of the set of items ofcollateral, recording a change in title for the one of the set of itemsof collateral, assessing the value of the one of the set of items ofcollateral, initiating inspection of the one of the set of items ofcollateral, initiating maintenance of the one of the set of items ofcollateral, initiating security for the one of the set of items ofcollateral, modifying terms and conditions for the one of the set ofitems of collateral, or the like.

One or more group of offset items of collateral may be identified (step5824) where each item in a group of offset items of collateral shares acommon attribute with at least one of the items of collateral.Marketplace information may then be monitored for data related to offsetitems of collateral (step 5826). The monitored marketplace informationregarding one or more offset items of collateral may be used to update avalue of an item of collateral (step 5828). The loan-entry in thedistributed ledger may be updated (5830) with the updated value of theitem of collateral.

Referring to FIG. 59, an example system 5900 for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement is depicted. The system 5900 mayinclude a controller 5901 which may include a plurality of AI circuits5920. The plurality of AI circuits 5920 may include a smart contractservices circuit 5910 to create and modify a smart lending contract 5912for a loan 5918. Smart lending contracts 5912 may include the terms andconditions 5914 for the loan 5918, a covenant specifying a requiredvalue of collateral, information regarding a loan 5918, items ofcollateral, information on lenders, including lender prioritiesincluding apportionment 5916 of the value of items of collateral amongthe lenders.

The plurality of AI circuits 5920 may include a valuation circuit 5902structured to determine one or more values 5908 for items of collateralbased on a valuation model 5909 and collateral data 5940. The valuationcircuit 5902 may include a collateral classification circuit 5903 toidentify items of offset collateral 5907 based on common attributes withitems of collateral used to secure a loan 5918. A market valuemonitoring circuit 5906 may receive marketplace information 5942regarding items of collateral and offset items of collateral 5907. Themarketplace information 5942 may be used by the valuation model 5909 indetermining values 5908 for items of collateral. The valuation circuit5902 may further include a valuation model improvement circuit 5904 toimprove the valuation model 5909 used to determine values 5908. Thevaluation model improvement circuit 5904 may utilize a training setincluding previously determined values 5908 for items of collateral anddata regarding the outcome of loans for which those items of collateralacted as security.

The plurality of AI circuits 5920 may include a loan management circuit5922 which may include a value comparison circuit 5928 to compare avalue 5908 of an item of collateral with a required value of the item ofcollateral as specified in a covenant of the loan, determining acollateral satisfaction value 5930. The smart contract services circuit5910 may determine, in response to the collateral satisfaction value5930, a term or a condition 5914 for a loan 5918, where the term ofconditions 5914 is related to a loan component such as a loan party, aloan collateral, a loan-related event, and a loan-related activity forthe smart lending contract 5912, and the like. The term of condition maybe a principal amount of the loan, a balance of the loan, a fixedinterest rate, a variable interest rate description, a payment amount, apayment schedule, a balloon payment schedule, a collateralspecification, a collateral substitution description, a description of aparty, a guarantee description, a guarantor description, a securitydescription, a personal guarantee, a lien, a foreclosure condition, adefault condition, a consequence of default, a covenant related to anyone of the foregoing, a duration of any one of the foregoing, and thelike. The term of condition may be a principal amount of debt, a balanceof debt, a fixed interest rate, a variable interest rate, a paymentamount, a payment schedule, a balloon payment schedule, a party, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, aconsequence of default, and the like. The smart contract servicescircuit 5910 may modify the smart lending contract 5912 to include newterms or conditions 5914, such as those determined in response to thecollateral satisfaction value 5930.

The loan management circuit 5922 may also include an automated agentcircuit 5924 to take an action 5926 based on the collateral satisfactionvalue 5930. The action 5926 may be a collateral-related action such asvalidating title for the item of collateral, recording a change in titlefor the item of collateral, assessing the value of the item ofcollateral, initiating inspection of the item of collateral, initiatingmaintenance of the item of collateral, initiating security for the

item of collateral, modifying terms and conditions for the item ofcollateral, and the like. The action 5926 may be a loan-related actionsuch as offering the loan, accepting the loan, underwriting the loan,setting an interest rate for a loan, deferring a payment requirement,modifying the interest rate for the loan, calling the loan, closing theloan, setting terms and conditions for the loan, providing noticesrequired to be provided to a borrower, foreclosing on property subjectto the loan, modifying terms and conditions for the loan, and the like.

The controller 5901 may also include a data collection circuit 5932 toreceive collateral data 5940 and determine a collateral event 5934. Thecollateral event 5934 and collateral data 5940 may then be reported by areporting circuit 5936. A blockchain service circuit 5938 may create andupdate blockchain data 5925 where a copy of the smart lending contract5912 is stored.

Referring to FIG. 60, an illustrative and non-limiting method 6000 forrobotic process automation of transactional, financial and marketplaceactivities is depicted. An example method may include receiving datarelated to an item or set of items of collateral (step 6002) where theitem(s) of collateral are acting as security for a loan. A value for theitem of collateral is determined (step 6004) based on received data anda valuation model. A smart lending contract is created (step 6006) whichspecifies information about the loan including a covenant specifying arequired value of collateral needed to secure the loan.

The value of the item(s) of collateral may be compared to the value ofcollateral specified in the covenant (step 6008) and a collateralsatisfaction value determined (step 6010), where the collateralsatisfaction value may be positive if the value of the collateralexceeds the required value of collateral or negative if the value ofcollateral is less than the required value of collateral. A loan-relatedaction may be implemented in response to the collateral satisfactionvalue (step 6012). A term or condition may be determined in response tothe collateral satisfaction value (step 6014) and the smart lendingcontract modified (step 6016).

The valuation model may be modified (step 6018) based on a first set ofvaluation determinations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security, using a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, a hybrid system of at least two of any ofthe foregoing, and the like.

A group of offset items of collateral may be identified (step 6020)based on common attributes with the collateral such as a category of theitem of collateral, an age of the item of collateral, a condition of theitem of collateral, a history of the item of collateral, an ownership ofthe item of collateral, a caretaker of the item of collateral, asecurity of the item of collateral, a condition of an owner of the itemof collateral, a lien on the item of collateral, a storage condition ofthe item of collateral, a geolocation of the item of collateral, and ajurisdictional location of the item of collateral. Marketplaceinformation such as may be monitored for data related to the offsetcollateral (step 6022) such as pricing or financial data and the smartlending contract modified in response to the marketplace information(step 6024). An action may be automatically initiated (step 6026) basedon the marketplace information. The action may include modifying a termof the loan, issuing a notice of default, initiating a foreclosureaction modifying a conditions of the loan, providing a notice to a partyof the loan, providing a required notice to a borrower of the loan,foreclosing on a property subject to the loan, validating title for theitem of collateral, recording a change in title for the item ofcollateral, assessing the value of the item of collateral, initiatinginspection of the item of collateral, initiating maintenance of the itemof collateral, initiating security for the item of collateral, andmodifying terms and conditions for the item of collateral, and the like.

Referring to FIG. 61, an illustrative and non-limiting example systemfor system for adaptive intelligence and robotic process automationcapabilities 6100 is depicted. The example system may include acontroller 6101 including a data collection circuit 6128 structured toreceive collateral data 6132 regarding a plurality of items ofcollateral used to secure a set of loans 6118. The data collectioncircuit 6128 may include an Internet of Things system, a camera system,a networked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, aninteractive crowdsourcing system, and the like. The items of collateralmay include a vehicle, a ship, a plane, a building, a home, a realestate property, an undeveloped land property, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, a tool, an item of machinery, an item of personalproperty, and the like. The set of loans may include an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, a subsidizedloan, and the like. The set of loans 6118 may be distributed among aplurality of borrowers as means of diversifying the risk of the loans.

The controller 6101 may also include a plurality of AI circuits 6144,including a collateral classification circuit 6120, to identify, fromamong the items of collateral, a group of collateral 6122 which relatedby sharing a common attribute, wherein the common attribute is among thereceived collateral data 6132, such as a type of the item of collateral,a category of the item of collateral, a value of the item of collateral,a price of a type of the item of collateral, a value of a type of theitem of collateral, a specification of the item of collateral, a productfeature set of the item of collateral, a model of the item ofcollateral, a brand of the item of collateral, a manufacturer of theitem of collateral, an age of the item of collateral, a liquidity of theitem of collateral, a shelf-life of the item of collateral, a usefullife of the item of collateral, a condition of the item of collateral, avaluation of the item of collateral, a status of the item of collateral,a context of the item of collateral, a state of the item of collateral,a storage location of the item of collateral, a history of the item ofcollateral, an ownership of the item of collateral, a caretaker of theitem of collateral, a security of the item of collateral, a condition ofan owner of the item of collateral, a lien on the item of collateral, astorage condition of the item of collateral, a maintenance history ofthe item of collateral, a usage history of the item of collateral, anaccident history of the item of collateral, a fault history of the itemof collateral, a history of ownership of the item of collateral, anassessment of the item of collateral, a geolocation of the item ofcollateral, a jurisdictional location of the item of collateral, and thelike. The collateral classification circuit 6120 may also identifyoffset collateral 6123 where items of offset collateral 6123 and theitems of collateral share a common attribute.

The reporting circuit 6134 may also report a collateral event 6130 basedon the collateral data 6132. An automated agent circuit 6108 mayautomatically perform an action 6109 based on the collateral event 6130.The action 6109 may be a collateral-related action such as validatingtitle for one of the plurality of items of collateral, recording achange in title for one of the plurality of items of collateral,assessing the value of one of the plurality of items of collateral,initiating inspection of one of the plurality of items of collateral,initiating maintenance of the one of the plurality of items ofcollateral, initiating security for one of the plurality of items ofcollateral, modifying terms and conditions for one of the plurality ofitems of collateral, and the like. The action 6109 may be a loan-relatedaction such as offering the loan, accepting the loan, underwriting theloan, setting an interest rate for a loan, deferring a paymentrequirement, modifying the interest rate for the loan, calling the loan,closing the loan, setting terms and conditions for the loan, providingnotices required to be provided to a borrower, foreclosing on propertysubject to the loan, modifying terms and conditions for the loan, andthe like.

The controller 6101 may also include a smart contract services circuit6110 to create a smart lending contract 6112 for an individual loan or aset of loans 6118 where the smart lending contract 6112 identifies asubset of collateral 6116, selected from the group of related items ofcollateral 6122 sharing a common attribute, to act as security for theset of loans 6118. The smart contract services circuit 6110 may alsoredefine the subset of collateral 6116 based on an updated value for anitem of collateral, thus rebalancing the items of collateral used for aset of loans based on the values of the collateral items. Theidentification of the subset of collateral 6116 may be identified inreal-time when the common attribute changes in real time (e.g. a statusof an item of collateral or whether collateral is in transit during adefined time period). Further, the smart contract services circuit 6110may determine a term or condition 6114 for the loan based on a value ofone of the items of collateral, where the term or the condition 6114 isrelated to a loan component such as a loan party, a loan collateral, aloan-related event, and a loan-related activity. The term or condition6114 may be a principal amount of the loan, a balance of the loan, afixed interest rate, a variable interest rate description, a paymentamount, a payment schedule, a balloon payment schedule, a collateralspecification, a collateral substitution description, a description of aparty, a guarantee description, a guarantor description, a securitydescription, a personal guarantee, a lien, a foreclosure condition, adefault condition, a consequence of default, a covenant related to anyone of the foregoing, a duration of any one of the foregoing, and thelike. The controller 6101 may also include a smart contract servicescircuit 6110 that uses blockchain data 6124, including a smart lendingcontract 6126 and a blockchain service circuit 6136, also usingblockchain data 6124, in communication with the smart contract servicescircuit 6110.

The controller may also include a valuation circuit 6102 to determine avalue 6140 for each item of collateral in the subset of items collateralbased on the received data and a valuation model 6142. A valuation modelimprovement circuit 6104 may modify the valuation model 6142 based on afirst set of valuation determinations for a first set of items ofcollateral and a corresponding set of loan outcomes having the first setof items of collateral as security. The valuation model improvementcircuit 6104 may include a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, a hybrid system including at least two ofthe foregoing, or the like. The valuation circuit 6102 may also includea market value data collection circuit 6106 to monitor and reportmarketplace information 6138 such as pricing or financial data relevantto offset collateral 6123 or a group of collateral 6122.

Referring to FIG. 62, a method 6200 for automated transactional,financial and marketplace activities. A method may include receivingdata related to an item of collateral (6202), identifying a group ofitems of collateral (6204) where the items in the group share a commonattribute or feature, identifying a subset of the group as security fora set of loans (6208) and creating a smart lending contract (6210) forthe set of loans where the smart lending contract identifies the subsetof group acting as security. The common attribute shared by the group ofitems of collateral may be in the received data.

The value of each item of collateral may be determined (6212) using thereceived data and a valuation model. The subset of collateral used assecurity may then be redefined based on the value of the different itemsof collateral (6214). A term of condition for at least one of the smartlending contracts may be determined (6218) based on the value for atleast one of the items of collateral in the subset of the group and thesmart lending contract modified to include the determined term orcondition (6220). Further, in some embodiments, the valuation model maybe modified (6222) based on a first set of valuation determinations fora first set of items of collateral and a corresponding set of loanoutcomes having the first set of items of collateral as security.

A group of offset items of collateral may be identified (step 6224)where each member of the group of offset items of collateral and thegroup of the plurality of items share a common attribute. An informationmarketplace may be monitored and marketplace information reported (step6226) for the group of offset items of collateral.

FIG. 63 depicts a system 6300 including a data collection circuit 6324structured to receive data 6302 related to a set of parties to a loan6312. The data collection circuit may be structured to receivecollateral-related data 6308 related to a set of items of collateral6314 acting as security for the loan and determine a condition of theset of items of collateral, where the change in the interest rate may bebased on a condition of the set of items of collateral. The item ofcollateral may be a vehicle, a ship, a plane, a building, a home, a realestate property, an undeveloped land property, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, a tool, an item of machinery, an item of personalproperty, and the like. The received data may include an attribute ofthe set of parties to the loan, where the change in the interest ratemay be based in part on the attribute. The data collection circuit mayinclude a system such as an Internet of Things circuit, an image capturedevice, a networked monitoring circuit, an internet monitoring circuit,a mobile device, a wearable device, a user interface circuit, aninteractive crowdsourcing circuit, and the like. For instance, the datacollection circuit may include an Internet of Things circuit 6354structured to monitor attributes of the set of parties to the loan. Thedata collection circuit may include a wearable device 6306 associatedwith at least one of the set of parties, where the wearable device isstructured to acquire human-related data 6304, and where the receiveddata includes at least a portion of the human-related data. The datacollection circuit may include a user interface circuit 6326 structuredto receive data from the parties of the loan and provide the data fromat least one of the parties of the loan as a portion of the receiveddata. The data collection circuit may include an interactivecrowdsourcing circuit 6338 structured to solicit data regarding at leastone of the set of parties of the loan, receive solicited data, andprovide at least a subset of the solicited data as a portion of thereceived data. The data collection circuit may include an internetmonitoring circuit 6340 structured to retrieve data related to theparties of the loan from at least one publicly available informationsite 6322. The system may include a smart contract circuit 6332structured to create a smart lending contract 6334 for the loan 6316.The loan may be a type selected from among loan types such as aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, a subsidizedloan, and the like. The smart contract circuit may be structured todetermine a term or a condition 6318 for the smart lending contractbased on the attribute and modify the smart lending contract to includethe term or the condition. The term or condition may be related to aloan component, such as a loan party, a loan collateral, a loan-relatedevent, a loan-related activity, and the like. The term or condition maybe a principal amount of the loan, a balance of the loan, a fixedinterest rate, a variable interest rate description, a payment amount, apayment schedule, a balloon payment schedule, a collateralspecification, a collateral substitution description, a description of aparty, a guarantee description, a guarantor description, a securitydescription, a personal guarantee, a lien, a foreclosure condition, adefault condition, a consequence of default, a covenant related to anyone of the foregoing, a duration of any one of the foregoing, and thelike. The system may include an automated agent circuit 6336 structuredto automatically perform a loan-related action 6320 in response to thereceived data, where the loan-related action is a change in an interestrate for the loan, and where the smart contract circuit may be furtherstructured to update the smart lending contract with the changedinterest rate. The system may include a valuation circuit 6328structured to determine, such as based on the received data and avaluation model 6330, a value for the at least one of the set of itemsof collateral. The smart contract circuit may be structured to determinea term or a condition for the smart lending contract based on the valuefor the at least one of the set of items of collateral and modify thesmart lending contract to include the term or the condition. The term orthe condition may be related to a loan component, such as a loan party,a loan collateral, a loan-related event, a loan-related activity, andthe like. The term or the condition may be a principal amount of theloan, a balance of the loan, a fixed interest rate, a variable interestrate description, a payment amount, a payment schedule, a balloonpayment schedule, a collateral specification, a collateral substitutiondescription, a description of a party, a guarantee description, aguarantor description, a security description, a personal guarantee, alien, a foreclosure condition, a default condition, a consequence ofdefault, a covenant related to any one of the foregoing, a duration ofany one of the foregoing, and the like. The valuation circuit mayinclude a valuation model improvement circuit 6342, where the valuationmodel improvement circuit may modify the valuation model, such as basedon a first set of valuation determinations 6344 for a first set of itemsof collateral and a corresponding set of loan outcomes having the firstset of items of collateral as security. The valuation model improvementcircuit may include a one system such as a machine learning system, amodel-based system, a rule-based system, a deep learning system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, a simulation system, a hybrid systemincluding at least two of the foregoing, and the like. The change in theinterest rate may be further based on the value for the at least one ofthe set of items of collateral. The valuation circuit may include amarket value data collection circuit 6346 structured to monitor andreport marketplace information 6343 for offset items of collateralrelevant to the value of the item of collateral. The market value datacollection circuit may be structured to monitor one of pricing orfinancial data for the offset items of collateral in at least one publicmarketplace and report the monitored one of pricing or financial data.The system may include a collateral classification circuit 63150structured to identify a group of offset items of collateral 6352, whereeach member of the group of offset items of collateral and at least oneof the set of items of collateral share a common attribute. The commonattribute may be a category of the item, an age of the item, a conditionof the item, a history of the item, an ownership of the item, acaretaker of the item, a security of the item, a condition of an ownerof the item, a lien on the item, a storage condition of the item, ageolocation of the item, a jurisdictional location of the item, and thelike.

FIG. 64 depicts a method 6400 including receiving data related to atleast one of a set of parties to a loan 6402, creating a smart lendingcontract for the loan 6404, performing a loan-related action in responseto the received data, wherein the loan-related action is a change in aninterest rate for the loan 6408, and updating the smart lending contractwith the changed interest rate 6410. The method may further includereceiving data related to a set of items of collateral acting assecurity for the loan 6414, determining a condition the set of items ofcollateral 6418, and performing a loan-related action in response to thecondition of the set of items of collateral, where the loan-relatedaction may be a change in interest rate for the loan 6420. The methodmay further include receiving data related to a set of items ofcollateral acting as security for the loan 6422, determining a conditionof at least one of the set of items of collateral 6424, determining aterm or a condition for the smart lending contract based on thecondition of the at least one of the set of items of collateral 6428,and modifying the smart lending contract to include the term or thecondition 6430. The method may include identifying a group of offsetitems of collateral wherein each member of the group of offset items ofcollateral and at least one of the set of items of collateral share acommon attribute, and monitoring the group of offset items of collateralin a public marketplace, and further may report the monitored data. Themethod may include changing, such as based on the monitored group ofoffset items of collateral, the interest rate of the loan secured by atleast one of the set of items of collateral.

FIG. 65 depicts a system 6500 including a data collection circuit 6518structured to acquire data 6502, from public sources of information 6504(e.g., a website, a news article, a social network, crowdsourcedinformation, and the like), related to at least one party of a set ofparties 6506 to a loan 6508 (e.g., primary lender, a secondary lender, alending syndicate, a corporate lender, a government lender, a banklender, a secured lender, bond issuer, a bond purchaser, an unsecuredlender, a guarantor, a provider of security, a borrower, a debtor, anunderwriter, an inspector, an assessor, an auditor, a valuationprofessional, a government official, an accountant, and the like). Thedata collection circuit may be further structured to receivecollateral-related data 6308 related to a set of items of collateral6512 acting as security for the loan and to determine a condition of atleast one of the set of items of collateral, wherein the change in theinterest rate is further based on the condition of the at least one ofthe set of items of collateral. The acquired data may include afinancial condition of the at least one party of the set of parties tothe loan. The financial condition may be determined based on at leastone attribute of the at least one party of the set of parties to theloan, the attribute selected from among the list of attributesconsisting of: a publicly stated valuation of the party, a set ofproperty owned by the party as indicated by public records, a valuationof a set of property owned by the party, a bankruptcy condition of theparty, a foreclosure status of the party, a contractual default statusof the party, a regulatory violation status of the party, a criminalstatus of the party, an export controls status of the party, an embargostatus of the party, a tariff status of the party, a tax status of theparty, a credit report of the party, a credit rating of the party, a website rating of the party, a set of customer reviews for a product of theparty, a social network rating of the party, a set of credentials of theparty, a set of referrals of the party, a set of testimonials for theparty, a set of behavior of the party, a location of the party, ageolocation of the party, a judicial location of the party, and thelike. The system may include a smart contract circuit 6524 structured tocreate a smart lending contract 6526 for the loan 6508. The smartcontract circuit may be structured to specify terms and conditions inthe smart lending contract, wherein one of a term or a condition in thesmart lending contract governs one of loan-related events orloan-related activities. The system may include an automated agentcircuit 6528 structured to automatically perform a loan-related action6516 in response to the acquired data, wherein the loan-related actionis a change in an interest rate for the loan, and wherein the smartcontract circuit is further structured to update the smart lendingcontract with the changed interest rate. The automated agent circuit maybe structured to identify an event relevant to the loan (e.g., a valueof the loan, a condition of collateral of the loan, or an ownership ofcollateral of the loan), based, at least in part, on the received data.The automated agent circuit may be structured to perform, in response tothe event relevant to the loan, an action selected from the list ofactions, such as offering the loan, accepting the loan, underwriting theloan, setting an interest rate for the loan, deferring a paymentrequirement, modifying an interest rate for the loan, validating titlefor at least one of the set of items of collateral, assessing the valueof at least one of the set of items of collateral, initiating inspectionof at least one of the set of items of collateral, setting or modifyingterms and conditions 6514 for the loan (e.g., a principal amount ofdebt, a balance of debt, a fixed interest rate, a variable interestrate, a payment amount, a payment schedule, a balloon payment schedule,a party, a guarantee, a guarantor, a security, a personal guarantee, alien, a duration, a covenant, a foreclose condition, a defaultcondition, and a consequence of default), providing a notice to one ofthe parties, providing a required notice to a borrower of the loan,foreclosing on a property subject to the loan, and the like. The loanmay include a loan type, such as an auto loan, an inventory loan, acapital equipment loan, a bond for performance, a capital improvementloan, a building loan, a loan backed by an account receivable, aninvoice finance arrangement, a factoring arrangement, a pay day loan, arefund anticipation loan, a student loan, a syndicated loan, a titleloan, a home loan, a venture debt loan, a loan of intellectual property,a loan of a contractual claim, a working capital loan, a small businessloan, a farm loan, a municipal bond, a subsidized loan, and the like.The acquired data may be related to the set of items of collateral suchas a vehicle, a ship, a plane, a building, a home, a real estateproperty, an undeveloped land property, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,a tool, an item of machinery, an item of personal property, and thelike. The system may include a valuation circuit 6520 structured todetermine, based on the acquired data and a valuation model 6522, avalue for at least one of the set of items of collateral. The valuationcircuit may include a valuation model improvement circuit 6530, wherethe valuation model improvement circuit modifies the valuation modelbased on a first set of valuation determinations 6532 for a first set ofitems of collateral and a corresponding set of loan outcomes having thefirst set of items of collateral as security. The valuation modelimprovement circuit may include a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, a hybrid system including at least two ofthe foregoing, and the like. The smart contract circuit may be furtherstructured to determine a term or a condition for the smart lendingcontract based on the value for the at least one of the set of items ofcollateral and modify the smart lending contract to include the term orthe condition, modify a term or condition of the loan based on themarketplace information for offset items of collateral relevant to thevalue of the item of collateral, and the like. The system may include acollateral classification circuit 65138 structured to identify a groupof offset items of collateral, wherein each member of the group ofoffset items 6540 of collateral and at least one of the set of items ofcollateral share a common attribute (e.g., a category of the item, anage of the item, a condition of the item, a history of the item, anownership of the item, a caretaker of the item, a security of the item,a condition of an owner of the item, a lien on the item, a storagecondition of the item, a geolocation of the item, a jurisdictionallocation of the item, and the like). The valuation circuit may furtherinclude a market value data collection circuit 6534 structured tomonitor and report marketplace information 6536 for offset items ofcollateral relevant to the value of the item of collateral, monitorpricing or financial data for the offset items of collateral in a publicmarketplace, and the like, and report the monitored pricing or financialdata.

FIG. 66 depicts a method 6600 including acquiring data, from publicsources, related to at least one of a set of parties to a loan, wherethe public sources of information may be selected from the list ofinformation sources consisting of a website, a news article, a socialnetwork, and crowdsourced information 6602. The method may includecreating a smart lending contract 6604. The method may includeperforming a loan-related action in response to the acquired data,wherein the loan-related action is a change in an interest rate for theloan 6606. The method may include updating the smart lending contractwith the changed interest rate 6608. The method may include receivingcollateral-related data related to a set of items of collateral actingas security for the loan 6610, and determining a condition of at leastone of the set of items of collateral, wherein the change in theinterest rate is further based on the condition of the at least one ofthe set of items of collateral 6612. The method may include identifyingan event relevant to the loan based, at least in part, on thecollateral-related data 6614, and performing, in response the eventrelevant to the loan, an action 6618, such as offering the loan,accepting the loan, underwriting the loan, setting an interest rate forthe loan, deferring a payment requirement, modifying an interest ratefor the loan, validating title for at least one of the set of items ofcollateral, assessing a value of at least one of the set of items ofcollateral, initiating inspection of at least one of the set of items ofcollateral, setting or modifying terms and conditions for the loan,providing a notice to one of the parties, providing a required notice toa borrower of the loan, foreclosing on a property subject to the loan,and the like. The method may include determining, based on at least oneof the collateral-related data or the acquired data, and a valuationmodel, a value for at least one of the set of items of collateral. Themethod may include determining at least one of a term or a condition forthe smart lending contract based on the value for the at least one ofthe set of items of collateral. The method may include modifying thesmart lending contract to include the at least one of the term or thecondition. The method may include modifying the valuation model based ona first set of valuation determinations for a first set of items ofcollateral and a corresponding set of loan outcomes having the first setof items of collateral as security. The method may include identifying agroup of offset items of collateral, wherein each member of the group ofoffset items of collateral and at least one of the set of items ofcollateral share a common attribute 6620, monitoring one of pricing dataor financial data for least one of the group offset items of collateralin at least one public marketplace 6622, reporting the monitored datafor the at least one of the group offset items of collateral 6624, andmodifying a term or condition of the loan based the reported monitoreddata 6628.

FIG. 67 depicts a system 6700 including a data collection circuit 6720structured to receive data 6702 relating to a status 6704 of a loan 6712and data relating to a set of items of collateral 6706 acting assecurity for the loan. The data collection circuit may monitor one ormore of the loan entities with a system such as an Internet of Thingssystem, a camera system, a networked monitoring system, an internetmonitoring system, a mobile device system, a wearable device system, auser interface system, and an interactive crowdsourcing system 67132.For instance, an interactive crowdsourcing system may include a userinterface 6734, the user interface configured to solicit informationrelated to one or more of the loan entities from a crowdsourcing site6718, and where the user interface is structured to allow one or more ofthe loan entities to input information one or more of the loan entities.In another instance, a networked monitoring system may include a networksearch circuit 6721 structured to search publicly available informationsites for information related one or more of the loan entities. Thesystem may include a blockchain service circuit 6744 structured tomaintain a secure historical ledger 6746 of events related to the loan,such as to interpret a plurality of access control features 6708corresponding to a plurality of parties 6710 associated with the loan.The system may include a loan evaluation circuit 67148 structured todetermine a loan status based on the received data. The data collectioncircuit may receive data related to one or more loan entities 6714,where the loan evaluation circuit may determine compliance with acovenant based on the data related to the one or more of the loanentities. The loan evaluation circuit may be structured to determine astate of performance for a condition of the loan based on the receiveddata and a status of the one or more of the loan entities, and whereinthe determination of the loan status is determined based in part on thestatus of the at least one or more of the loan entities and the state ofperformance of the condition for the loan. For instance, the conditionof the loan may relate to at least one of a payment performance and asatisfaction on a covenant. The data collection circuit may include amarket data collection circuit 6736 structured to receive financial data6738 regarding at least one of the plurality of parties associated withthe loan. The loan evaluation circuit may be structured to determine afinancial condition of the least one of the plurality of partiesassociated with the loan based on the received financial data, where theat least one of the plurality of parties may be a primary lender, asecondary lender, a lending syndicate, a corporate lender, a governmentlender, a bank lender, a secured lender, bond issuer, a bond purchaser,an unsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, an accountant, and thelike. The received financial data may relate to an attribute of theentity for one of the plurality of parties, such as a publicly statedvaluation of the party, a set of property owned by the party asindicated by public records, a valuation of a set of property owned bythe party, a bankruptcy condition of the party, a foreclosure status ofthe entity, a contractual default status of the entity, a regulatoryviolation status of the entity, a criminal status of the entity, anexport controls status of the entity, an embargo status of the entity, atariff status of the entity, a tax status of the entity, a credit reportof the entity, a credit rating of the entity, a web site rating of theentity, a set of customer reviews for a product of the entity, a socialnetwork rating of the entity, a set of credentials of the entity, a setof referrals of the entity, a set of testimonials for the entity, a setof behavior of the entity, a location of the entity, a geolocation ofthe entity, and the like. The system may include a smart contractcircuit 6726 structured to create a smart lending contract 6728 for theloan. The smart contract circuit may be structured to determine a termor a condition for the smart lending contract based on the value for theat least one of the set of items of collateral and modify the smartlending contract to include the term or the condition, where the termsand conditions may be a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, a consequence ofdefault, and the like. The system may include an automated agent circuit6730 structured to perform a loan-action 6716 based on the loan status,where the blockchain service circuit may be structured to update thehistorical ledger of events with the loan action. The system may includea valuation circuit 6722 structured to determine, based on the receiveddata and a valuation model 6724, a value for at least one of the set ofitems of collateral. The valuation circuit may include a valuation modelimprovement circuit 6740, where the valuation model improvement circuitmodifies the valuation model based on a first set of valuationdeterminations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security. The valuation model improvement circuit mayinclude a machine learning system, a model-based system, a rule-basedsystem, a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system. The valuation circuit may include amarket value data collection circuit 6742 structured to monitor andreport marketplace information for offset items of collateral relevantto the value of the item of collateral. The market value data collectioncircuit may be further structured to monitor pricing or financial datafor the offset items of collateral in a public marketplace, such as toreport the monitored pricing or financial data. The smart contractcircuit may be further structured to modify a term or condition of theloan based on the marketplace information for offset items of collateralrelevant to the value of the item of collateral. The system may includea collateral classification circuit 67150 structured to identify a groupof offset items of collateral 6752, where each member of the group ofoffset items of collateral and at least one of the set of items ofcollateral may share a common attribute. The common attribute may be acategory of the item of collateral, an age of the item of collateral, acondition of the item of collateral, a history of the item ofcollateral, an ownership of the item of collateral, a caretaker of theitem of collateral, a security of the item of collateral, a condition ofan owner of the item of collateral, a lien on the item of collateral, astorage condition of the item of collateral, a geolocation of the itemof collateral, a jurisdictional location of the item of collateral, andthe like.

FIG. 68 depicts a method 6800 including maintaining a secure historicalledger of events related to a loan 6802, receiving data relating to astatus of the loan 6804, receiving data related to a set of items ofcollateral acting as security of the loan 6808, determining a status ofthe loan 6810, performing a loan-action based on the loan status 6812and updating the historical ledger of events related to the loan 6814.The method may further include receiving data related to one or moreloan entities 6818 and determining compliance with a covenant of theloan based on the data received 6820. The method may further includedetermining a state of performance for a condition of the loan, wherethe determination of the loan status is based on part on the state ofperformance of the condition of the loan. The method may further includereceiving financial data related to at least one party to the loan. Themethod may further include determining a financial condition of the atleast one party to the loan based on the financial data. The method mayfurther include determining a value for at least one set of items ofcollateral based on the received data and a valuation model. The methodmay further include determining at least one of a term or a conditionfor the loan based on the value of the at least one of the items ofcollateral 6822 and modifying a smart lending contract to include the atleast one of the term or the condition 6824. The method may include 270identifying a group of offset items of collateral, where each member ofthe group of offset items of collateral and at least one of the set ofitems of collateral share a common attribute 6828, receiving datarelated to the group of offset items of collateral, wherein thedetermination of the value for the at least one set of items ofcollateral is partially based on the received data related to the groupof offset items of collateral 6830.

Referring to FIG. 69, an illustrative and non-limiting example smartcontract system for managing collateral for a loan 6900 is depicted. Theexample system may include a controller 69101. The controller 69101 mayinclude a data collection circuit 6912 structured to monitor a status ofa loan 6930 and of a collateral 6928 for the loan, and severalartificial intelligence circuits 6942 including a smart contract circuit6922 structured to process information from the data collection circuit6912 and automatically initiate at least one of a substitution, aremoval, or an addition of one or items from the collateral for the loanbased on the information and a smart lending contract 6931 in responseto at least one of the status of the loan or the status of thecollateral for the loan; and a blockchain service circuit 6958structured to interpret a plurality of access control features 6980corresponding to at least one party associated with the loan and recordthe at least one substitution, removal, or addition in a distributedledger 6940 for the loan. The data collection circuit may furtherinclude at least one other system 6962 selected from the systemsconsisting of: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

A status of the loan 6930 may be determined based on the status of atleast one of an entity (e.g., user 6951) related to the loan and a stateof a performance of a condition for the loan. State of the performanceof the condition may relate to at least one of a payment performance ora satisfaction of a covenant for the loan. The status of the loan may bedetermined based on a status of at least one entity related to the loanand a state of performance of a condition for the loan; and theperformance of the condition may relate to at least one of a paymentperformance or a satisfaction of a covenant for the loan. The datacollection circuit 6912 may be further structured to determinecompliance with the covenant by monitoring the at least one entity. Whenthe at least one entity is a party to the loan, the data collectioncircuit 6912 may monitor a financial condition of at least one entitythat is a party to the loan. The condition for the loan may include afinancial condition for the loan, and wherein the state of performanceof the financial condition may be determined based on an attributeselected from the attributes consisting of: a publicly stated valuationof the at least one entity, a property owned by the at least one entityas indicated by public records, a valuation of a property owned by theat least one entity, a bankruptcy condition of the at least one entity,a foreclosure status of the at least one entity, a contractual defaultstatus of the at least one entity, a regulatory violation status of theat least one entity, a criminal status of the at least one entity, anexport controls status of the at least one entity, an embargo status ofthe at least one entity, a tariff status of the at least one entity, atax status of the at least one entity, a credit report of the at leastone entity, a credit rating of the at least one entity, a website ratingof the at least one entity, a plurality of customer reviews for aproduct of the at least one entity, a social network rating of the atleast one entity, a plurality of credentials of the at least one entity,a plurality of referrals of the at least one entity, a plurality oftestimonials for the at least one entity, a behavior of the at least oneentity, a location of the at least one entity, a geolocation of the atleast one entity, and a relevant jurisdiction for the at least oneentity.

The party to the loan may be selected from the parties consisting of: aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official,and an accountant.

The data collection circuit 6912 may be further structured to monitorthe status of the collateral of the loan based on at least one attributeof the collateral selected from the attributes consisting of: a categoryof the collateral, an age of the collateral, a condition of thecollateral 6911, a history of the collateral, a storage condition of thecollateral, and a geolocation of the collateral.

The controller 69101 may include a valuation circuit 6944 which may bestructured to use a valuation model 6952 to determine a value for thecollateral based on the status of the collateral for the loan. The smartcontract circuit 6922 may initiate the at least one substitution,removal or addition of one or more items from the collateral for theloan to maintain a value of collateral within a predetermined range.

The valuation circuit 6944 may further include a transactions outcomeprocessing circuit 6964 structured to interpret outcome data 6910relating to a transaction in collateral and iteratively improve 6950 thevaluation model in response to the outcome data.

The valuation circuit 6944 may further include a market value datacollection circuit 6948 structured to monitor and report on marketplaceinformation relevant to a value of collateral. The market value datacollection circuit 6948 may monitor pricing data or financial data foran offset collateral item 6934 in at least one public marketplace.

The market value data collection circuit 6948 is further structured toconstruct a set of offset collateral items 6934 used to value an item ofcollateral may be constructed using a clustering circuit 6932 of thecontroller 69101 based on an attribute of the collateral. The attributesmay be selected from among a category of the collateral, an age of thecollateral, a condition of the collateral, a history of the collateral,a storage condition of the collateral, and a geolocation of thecollateral.

Terms and conditions 6924 for the loan may include at least one memberselected from the group consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a party, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, and a consequence of default.

The smart contract circuit may further include or be in communicationwith a loan management circuit 6960 structured to specify terms andconditions of the smart lending contract 6931 that governs at least oneof loan terms and conditions, a loan-related event 6939 or aloan-related activity or action 6938.

Referring to FIG. 70, an example smart contract method 7000 for managingcollateral for a loan is depicted. The example method may includemonitoring a status of a loan and of a collateral for the loan (step7002); processing information from the monitoring (step 7004);automatically initiating at least one of a substitution, a removal, oran addition of one or more items from the collateral for the loan basedon the information (step 7008); and interpreting a plurality of accesscontrol features corresponding to at least one party associated with theloan (step 7010) and recording the at least one substitution, removal,or addition in a distributed ledger for the loan (step 7012). A statusof the loan may be determined based on the status of at least one of anentity related to the loan and a state of a performance of a conditionfor the loan.

The method may further include interpreting information from themonitoring (step 7014) and determining a value with a valuation modelfor a set of collateral based on at least one of the status of the loanor the collateral for the loan (step 7018). The at least onesubstitution, removal, or addition may be to maintain a value ofcollateral within a predetermined range. The method may further includeinterpreting outcome data relating to a transaction of one of thecollateral or an offset collateral (step 7020) and iteratively improvingthe valuation model in response to the outcome data (step 7022). Themethod may further include monitoring and reporting on marketplaceinformation relevant to a value of collateral (step 7024).

The method may further include monitoring pricing data or financial datafor an offset collateral item in at least one public marketplace (step7028).

The method may further include specifying terms and conditions of asmart contract that governs at least one of terms and conditions for theloan, a loan-related event or a loan-related activity (step 7030).

Referring to FIG. 71, an illustrative and non-limiting examplecrowdsourcing system for validating conditions of collateral or aguarantor for a loan 7100 is depicted. The example system may include acontroller 71101. The controller 71101 may include a data collectioncircuit 7112, a user interface 7154, and several artificial intelligencecircuits 7142 including a smart contract circuit 7122, robotic processautomation circuit 7174, a crowdsourcing request circuit 7160, acrowdsourcing communications circuit 7162, a crowdsourcing publishingcircuit 7164, and a blockchain service circuit 7158.

The crowdsourcing request circuit 7160 may be structured to configure atleast one parameter of a crowdsourcing request 7168 related to obtaininginformation 7104 on condition 7111 of a collateral 7102 for a loan 7130or a condition of a guarantor for the loan 7130. It may also enable aworkflow by which a human user 7106 enters the at least one parameter toestablish the crowdsourcing request. The at least one parameter mayinclude a type of requested information, the reward, and a condition forreceiving the reward. The reward may be selected from selected from therewards consisting of a financial reward, a token, a ticket, acontractual right, a cryptocurrency, a plurality of reward points, acurrency, a discount on a product or service, and an access right.

The crowdsourcing publishing circuit 7164 may be configured to publishthe crowdsourcing request 7168 to a group of information suppliers.

The crowdsourcing communications circuit 7162 may be structured tocollect and process at least one response 7172 from the group ofinformation suppliers 7170, and to provide a reward 7180 to at least oneof the group of information suppliers in response to a successfulinformation supply event.

The crowdsourcing communications circuit 7162 further includes a smartcontract circuit 7122 structured to manage the reward 7180 bydetermining the successful information supply event in response to theat least one parameter configured for the crowdsourcing request 7168,and to automatically allocate the reward 7180 to the at least one of thegroup of information suppliers 7170 in response to the successfulinformation supply event. It may also be structured to process the atleast one response 7172 and, in response, automatically undertake anaction related to the loan. The action may be at least one of aforeclosure action, a lien administration action, an interest-ratesetting action, a default initiation action, a substitution ofcollateral, or a calling of the loan.

The loan 7130 may include at least one loan type selected from the loantypes consisting of: an auto loan, an inventory loan, a capitalequipment loan, a bond for performance, a capital improvement loan, abuilding loan, a loan backed by an account receivable, an invoicefinance arrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The crowdsourcing request circuit 7160 may be further structured toconfigure at least one further parameter of the crowdsourcing request7168 to obtain information on a condition 7111 of a collateral for theloan.

The collateral 7102 may include at least one item selected from theitems consisting of: a vehicle, a ship, a plane, a building, a home,real estate property, undeveloped land, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

The condition 7111 of collateral may be determined based on an attributeselected from the attributes consisting of: a quality of the collateral,a condition of the collateral, a status of a title to the collateral, astatus of a possession of the collateral, and a status of a lien on thecollateral. When the collateral is an item, the condition may bedetermined based on an attribute selected from the attributes consistingof: a new or used status of the item, a type of the item, a category ofthe item, a specification of the item, a product feature set of theitem, a model of the item, a brand of the item, a manufacturer of theitem, a status of the item, a context of the item, a state of the item,a value of the item, a storage location of the item, a geolocation ofthe item, an age of the item, a maintenance history of the item, a usagehistory of the item, an accident history of the item, a fault history ofthe item, an ownership of the item, an ownership history of the item, aprice of a type of the item, a value of a type of the item, anassessment of the item, and a valuation of the item.

The blockchain service circuit 7158 may be structured to recordidentifying information and the at least one parameter of thecrowdsourcing request, the at least one response to the crowdsourcingrequest, and a reward description in a distributed ledger 7140.

The robotic process automation circuit 7174 may be structured to, basedon training on a training data set 7178 comprising human userinteractions with at least one of the crowdsourcing request circuit orthe crowdsourcing communications circuit, to configure the crowdsourcingrequest based on at least one attribute of the loan. The at least oneattribute of the loan may be obtained from a smart contract circuit 7122that manages the loan. The training data set 7178 may further includeoutcomes 7110 from a plurality of crowdsourcing requests.

The robotic process automation circuit 7174 may be further structured todetermine a reward 7180.

The robotic process automation circuit 7174 may be further structured todetermine at least one domain to which the crowdsourcing publishingcircuit 7164 publishes the crowdsourcing request 7168.

Referring to FIG. 72, provided herein is a crowdsourcing method 7200 forvalidating conditions of collateral or a guarantor for a loan. At leastone parameter of a crowdsourcing request may be configured to obtaininformation on a condition of a collateral for a loan or a condition ofa guarantor for the loan (step 7202). The crowdsourcing request may bepublished to a group of information suppliers (step 7204). At least oneresponse to the crowdsourcing request may be collected and processed(step 7208). A reward may be provided to at least one successfulinformation supplier of the group of information suppliers in responseto a successful information supply event (step 7210). A rewarddescription may be published to at least a portion of the group ofinformation suppliers in response to the successful information supplyevent (step 7212). The reward may be automatically allocated to at leastone of the group of information suppliers in response to the successfulinformation supply event (step 7230). The method may further includerecording identifying information and the at least one parameter of thecrowdsourcing request, the at least one response to the crowdsourcingrequest, and a reward description in a distributed ledger for thecrowdsourcing request (step 7214). A graphical user interface may beconfigured to enable a workflow by which a human user enters the atleast one parameter to establish the crowdsourcing request (step 7218).An action related to the loan may be automatically undertaken inresponse to the successful information supply event (step 7220). Arobotic process automation circuit may be trained on a training data setcomprising a plurality of outcomes corresponding to a plurality of thecrowdsourcing requests, and operating the robotic process automationcircuit to iteratively improve the crowdsourcing request (step 7222). Atleast one attribute of the loan may be provided to the robotic processautomation circuit in order to configure the crowdsourcing request (step7224). Configuring the crowdsourcing request may include determining areward. At least one attribute of the loan may be provided to therobotic process automation circuit in order to determine at least onedomain to which to publish the crowdsourcing request (step 7228).

Referring to FIG. 73, an illustrative and non-limiting example smartcontract system 7300 for modifying a loan 7330 is depicted. The examplesystem may include a controller 7301. The controller 7301 may include adata collection circuit 7312, a valuation circuit 7344, and severalartificial intelligence circuits 7342 including a smart contract circuit7322, a clustering circuit 7332, and a loan management circuit 7360. Thedata collection circuit 7312 may be structured to determine locationinformation corresponding to each one of a plurality of entitiesinvolved in a loan. The smart contract circuit 7322 may be structured todetermine a jurisdiction for at least one of the plurality of entitiesin response to the location information. The smart contract circuit 7322may be structured to automatically undertake a loan-related action 7338for the loan based at least in part on the jurisdiction for at least oneof the plurality of entities.

The smart contract circuit 7322 may be further structured toautomatically undertake the loan-related action in response to a firstone of the plurality of entities being in a first jurisdiction, and asecond one of the plurality of entities being in a second jurisdiction.

The smart contract circuit 7322 may be further structured toautomatically undertake the loan-related action in response to one ofthe plurality of entities moving from a first jurisdiction to a secondjurisdiction.

The loan-related action 7338 may include at least one loan-relatedaction selected from the loan-related actions consisting of: offeringthe loan, accepting the loan, underwriting the loan, setting an interestrate for the loan, deferring a payment requirement, modifying aninterest rate for the loan, validating title for collateral, recording achange in title, assessing a value of collateral, initiating inspectionof collateral, calling the loan, closing the loan, setting terms andconditions for the loan, providing notices required to be provided to aborrower, foreclosing on property subject to the loan, and modifyingterms and conditions for the loan.

The smart contract circuit 7322 may be further structured to process aplurality of jurisdiction-specific regulatory requirements 7368, such asrequirements related to notice, and to provide an appropriate notice toa borrower based on a jurisdiction corresponding to at least one entityselected from the entities consisting of a lender, a borrower, fundsprovided via the loan, a repayment of the loan, or a collateral for theloan.

The smart contract circuit 7322 may be further structured to process aplurality of jurisdiction-specific regulatory requirements 7368, such asrequirement related to foreclosure, and to provide an appropriateforeclosure notice to a borrower based on a jurisdiction of at least oneof a lender, a borrower, funds provided via the loan, a repayment of theloan, and a collateral for the loan.

The smart contract circuit 7322 may be further structured to process aplurality of jurisdiction-specific rules 7370 for setting terms andconditions 7324 of the loan and to configure a smart contract 7331 basedon a jurisdiction corresponding to at least one entity selected from theentities consisting of: a borrower, funds provided via the loan, arepayment of the loan, and a collateral for the loan.

The smart contract circuit 7322 may be further structured to determinean interest rate for the loan to cause the loan to comply with a maximuminterest rate limitation applicable in a jurisdiction corresponding to aselected one of the plurality of entities.

The data collection circuit 7312 may be further structured to monitoroutcome data 7310 and a condition 7311 of a collateral for the loan,such as with collateral data 7304, and wherein the smart contractcircuit is further structured to determine the interest rate for theloan in response to the condition of the collateral for the loan.

The data collection circuit 7312 may be further structured to monitor anattribute of at least one of the plurality of entities that are party tothe loan, and wherein the smart contract circuit is further structuredto determine the interest rate for the loan in response to theattribute.

The smart contract circuit 7322 may further include a loan managementcircuit 7360 for specifying terms and conditions of smart contracts thatgovern at least one of loan terms and conditions 7324, loan-relatedevents 7339 or loan-related activities 7372.

The loan may include at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring management, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

Terms and conditions for the loan may each include at least one memberselected from the group consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a party, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, and a consequence of default.

The data collection circuit 7312 may further include at least one othersystem 7362 selected from the systems consisting of: an Internet ofThings system, a camera system, a networked monitoring system, aninternet monitoring system, a mobile device system, a wearable devicesystem, a user interface system, and an interactive crowdsourcingsystem.

The valuation circuit 7344 may be structured to use a valuation model7352 to determine a value for a collateral for the loan based on thejurisdiction corresponding to at least one of the plurality of entities.The valuation model 7352 may be a jurisdiction-specific valuation model,and wherein the jurisdiction corresponding to at least one of theplurality of entities comprises a jurisdiction corresponding to at leastone entity selected from the entities consisting of: a lender, aborrower, funds provided pursuant to the loan, a delivery location offunds provided pursuant to the loan, a payment of the loan, and acollateral for the loan.

At least one of the terms and conditions for the loan may be based onthe value of the collateral for the loan.

The collateral may include at least one item selected from the itemsconsisting of: a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty.

The valuation circuit 7344 may further include a transactions outcomeprocessing circuit 7364 structured to interpret outcome data relating toa transaction in collateral and iteratively improve 7350 the valuationmodel in response to the outcome data.

The valuation circuit 7344 may further include a market value datacollection circuit 7348 structured to monitor and report on marketplaceinformation relevant to a value of collateral. The market value datacollection circuit may monitor pricing or financial data for an offsetcollateral item in at least one public marketplace. A set of offsetcollateral items 7334 for valuing an item of collateral may beconstructed using the clustering circuit 7332 based on an attribute ofthe collateral. The attribute may be selected from among a category ofthe collateral, an age of the collateral, a condition of the collateral,a history of the collateral, a storage condition of the collateral, anda geolocation of the collateral.

Referring to FIG. 74, provided herein is a smart contract method 7400for modifying a loan. An example method may include monitoring locationinformation corresponding to each one of a plurality of entitiesinvolved in a loan (step 7402); processing a location information aboutthe entities and automatically undertaking a loan-related action for theloan based at least in part on the location information (step 7404). Theexample method includes processing a number of jurisdiction-specificregulatory notice requirements and providing an appropriate notice to aborrower based on a location of the lender, a borrower, funds providedvia the loan, a repayment of the loan, and/or a collateral for the loan(step 7408). The example method includes processing a number ofjurisdiction-specific rules for setting terms and conditions of theloan, and configuring a smart contract based on a location of thelender, a borrower, funds provided via the loan, a repayment of theloan, and/or a collateral for the loan (step 7410). The example methodfurther includes determining an interest rate of the loan to cause theloan to comply with a maximum interest rate limitation applicable in ajurisdiction (step 7412). The example method includes monitoring atleast one of a condition of a number of collateral items for the loan oran attribute of one of the entities that are a party to the loan, wherethe condition or the attribute is used to determine an interest rate(step 7414). The example method includes specifying terms and conditionsof smart contract(s) that govern at least one of the terms andconditions, loan-related events, or loan-related activities (step 7418).The example method includes interpreting the location information andusing a valuation model to determine a value for a number of collateralitems for the loan based on the location information (step 7420). Theexample method includes interpreting outcome data relating to atransaction in collateral, and iteratively improving the valuation modelin response to the outcome data (step 7422). The example method includesmonitoring and reporting on marketplace information relevant to a valueof collateral (step 7424).

A plurality of jurisdiction-specific requirements based on ajurisdiction of a relevant one of the plurality of entities may beprocessed, and performing at least one operation may be selected fromthe operations consisting of: providing an appropriate notice to aborrower in response to the plurality of jurisdiction-specificrequirements comprising regulatory notice requirements; setting specificrules for setting terms and conditions of the loan in response to theplurality of jurisdiction-specific requirements comprisingjurisdiction-specific rules for terms and conditions of the loan;determining an interest rate for the loan to cause the loan to complywith a maximum interest rate limitation in response to the plurality ofjurisdiction-specific requirements comprising a maximum interest ratelimitation; and wherein the relevant one of the plurality of entitiescomprises at least one entity selected from the entities consisting of:a lender, a borrower, funds provided pursuant to the loan, a repaymentof the loan, and a collateral for the loan (step 7408).

At least one of a condition of a plurality of collateral for the loan oran attribute of at least one of the plurality of entities that are partyto the loan may be monitored, wherein the condition or the attribute isused to determine an interest rate (step 7414).

A valuation model may be operated to determine a value for a collateralfor the loan based on the jurisdiction for at least one of the pluralityof entities (step 7420).

Outcome data relating to a transaction in collateral may be interpretedand the valuation model may be iteratively improved in response to theoutcome data (step 7422).

Referring now to FIG. 75, an illustrative and non-limiting example smartcontract system for modifying a loan 7500 is depicted. The examplesystem may include a controller 75101. The controller 75101 may includea data collection circuit 7512, a valuation circuit 7544, and severalartificial intelligence circuits 7542 including a smart contract circuit7522, a clustering circuit 7532, and a loan management circuit 7560.

The data collection circuit 7512 may be structured to monitor andcollect information about at least one entity involved in a loan 7530.The smart contract circuit 7522 may be structured to automaticallyrestructure a debt related to the loan based on the monitored andcollected information about the at least one entity involved in theloan. The monitored and collected information may include a condition ofa collateral 7511 for the loan, or according to at least one rule thatis based on a covenant of the loan and wherein the restructuring occursupon an event that is determined with respect to the at least one entitythat relates to the covenant, or restructuring may be based on anattribute of the at least one entity that is monitored by the datacollection circuit. The event may be a failure of collateral for theloan to exceed a required fractional value of a remaining balance of theloan, or a default of a buyer with respect to the covenant.

The smart contract circuit 7522 may be further structured to determinethe occurrence of an event based on a covenant of the loan and themonitored and collected information about the at least one entityinvolved in the loan, and to automatically restructure the debt inresponse to the occurrence of the event.

The smart contract circuit 7522 may further include a loan managementcircuit 7560 which may be structured to specify terms and conditions ofa smart contract that governs at least one of loan terms and conditions7524, a loan-related event 7539 or a loan-related activity 7572.

The loan may include at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

Terms and conditions for the loan may include at least one memberselected from the group consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of collateral, a specification of substitutability ofcollateral, a party, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, and a consequence of default.

The data collection circuit 7512 may further include at least one othersystem 7562 selected from the systems consisting of: an Internet ofThings system, a camera system, a networked monitoring system, aninternet monitoring system, a mobile device system, a wearable devicesystem, a user interface system, and an interactive crowdsourcingsystem.

The valuation circuit 7544 may be structured to use a valuation model7552 to determine a value for a collateral based on the monitored andcollected information about the at least one entity involved in theloan. The smart contract circuit may be further structured toautomatically restructure the debt based on the value for thecollateral.

The collateral may be at least one item selected from the itemsconsisting of: a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty.

The valuation circuit 7544 may further include a transactions outcomeprocessing circuit 7564 structured to interpret outcome data 7510relating to a transaction in collateral and iteratively improve 7550 thevaluation model in response to the outcome data.

The valuation circuit 7544 may further include a market value datacollection circuit 7548 structured to monitor and report on marketplaceinformation relevant to a value of collateral. The market value datacollection circuit 7548 monitors pricing or financial data for an offsetcollateral item 7534 in at least one public marketplace. A set of offsetcollateral items 7534 for valuing an item of collateral may beconstructed using a clustering circuit 7532 based on an attribute of thecollateral. The attribute may be selected from among a category of thecollateral, an age of the collateral, a condition of the collateral, ahistory of the collateral, a storage condition of the collateral, and ageolocation of the collateral.

Referring now to FIG. 76, an illustrative and non-limiting example smartcontract method for modifying a loan 7600 is depicted. The methodincludes monitoring and collecting information about at least one entityinvolved in a loan (step 7602); processing information from themonitoring of the at least one entity (step 7604); and automaticallyrestructuring a debt related to the loan based on the monitored andcollected information about the at least one entity (step 7608).Determining the occurrence of an event may be based on a covenant of theloan and the monitored and collected information about the at least oneentity involved in the loan, and the method may include automaticallyrestructuring the debt in response to the occurrence of the event.

Terms and conditions of a smart contract that governs at least one ofloan terms and conditions, a loan-related event and a loan-relatedactivity may be specified (step 7610).

Operating a valuation model to determine a value for a collateral basedon the monitored and collected information about the at least one entityinvolved in the loan (step 7612).

Outcome data relating to a transaction in collateral may be interpretedand the valuation model may be iteratively improved in response to theoutcome data (step 7614).

The method may further include monitoring and reporting on marketplaceinformation relevant to a value of collateral (step 7618).

Pricing or financial data for an offset collateral item may be monitoredin at least one public marketplace (step 7620).

A set of offset collateral items for valuing an item of collateral maybe constructed using a similarity clustering algorithm based on anattribute of the collateral (step 7622).

Referring now to FIG. 77, an illustrative and non-limiting example smartcontract system for modifying a loan 7700 is depicted. The examplesystem may include a controller 77101. The controller 77101 may includea data collection circuit 7712, a social networking input circuit 7744,a social network data collection circuit 7732, and several artificialintelligence circuits 7742 including a smart contract circuit 7722, aguarantee validation circuit 7798, and a robotic process automationcircuit 7748.

The social network data collection circuit 7732 may be structured tocollect data, such as outcome data 7710, using a plurality of algorithmsthat are configured to monitor social network information about anentity 7764 involved in a loan 7730 in response to the loan guaranteeparameter and to identify data collection outcomes. The socialnetworking input circuit 7744 may be structured to interpret a loanguarantee parameter. The guarantee validation circuit 7798 may bestructured to validate a guarantee for the loan in response to themonitored social network information.

The loan guarantee parameter may include a financial condition of theentity, wherein the entity is a guarantor for the loan.

The guarantee validation circuit 7798 may be further structured todetermine the financial condition may be determined based on at leastone attribute selected from the attributes consisting of: a publiclystated valuation of the entity, a property owned by the entity asindicated by public records, a valuation of a property owned by theentity, a bankruptcy condition of the entity, a foreclosure status ofthe entity, a contractual default status of the entity, a regulatoryviolation status of the entity, a criminal status of the entity, anexport controls status of the entity, an embargo status of the entity, atariff status of the entity, a tax status of the entity, a credit reportof the entity, a credit rating of the entity, a website rating of theentity, a plurality of customer reviews for a product of the entity, asocial network rating of the entity, a plurality of credentials of theentity, a plurality of referrals of the entity, a plurality oftestimonials for the entity, a plurality of behaviors of the entity, alocation of the entity, a jurisdiction of the entity, and a geolocationof the entity.

The loan may include at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The data collection circuit 7712 may be structured to obtain informationabout a condition 7711 of a collateral for the loan, wherein thecollateral comprises at least one item selected from the itemsconsisting of: a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty and wherein the guarantee validation circuit is furtherstructured to validate the guarantee of the loan in response to thecondition of the collateral for the loan.

The condition 7711 of collateral may include a condition attributeselected from the group consisting of a quality of the collateral, astatus of title to the collateral, a status of possession of thecollateral, a status of a lien on the collateral, a new or used status,a type, a category, a specification, a product feature set, a model, abrand, a manufacturer, a status, a context, a state, a value, a storagelocation, a geolocation, an age, a maintenance history, a usage history,an accident history, a fault history, an ownership, an ownershiphistory, a price, an assessment, and a valuation. Conditions may bestored as collateral data 7704.

The social networking input circuit 7744 may be further structured toenable a workflow by which a human user enters the loan guaranteeparameter to establish a social network data collection and monitoringrequest.

The smart contract circuit 7722 may be structured to automaticallyundertake an action related to the loan in response to the validation ofthe loan. The action may be related to the loan is in response to theloan guarantee not being validated, and wherein the action comprises atleast one action selected from the actions consisting of: a foreclosureaction, a lien administration action, an interest-rate adjustmentaction, a default initiation action, a substitution of collateral, acalling of the loan, and providing an alert to a second entity involvedin the loan.

The robotic process automation circuit 7748 may be structured to, basedon iteratively training on a training data set 7746 comprising humanuser interactions with the social network data collection circuit,configure the loan guarantee parameter based on at least one attributeof the loan. The at least one attribute of the loan 7730 may be obtainedfrom a smart contract circuit that manages the loan.

The training data set 7746 may further include outcomes from a pluralityof social network data collection and monitoring requests performed bythe social network data collection circuit.

The robotic process automation circuit 7748 may be further structured todetermine at least one domain to which the social network datacollection circuit will apply.

Training may include training the robotic process automation circuit7748 to configure the plurality of algorithms.

Referring now to FIG. 78, an illustrative and non-limiting example smartcontract method for modifying a loan 7800 is depicted. A loan guaranteeparameter may be interpreted (step 7801). Data may be collected using aplurality of algorithms that are configured to monitor social networkinformation about an entity involved in a loan in response to the loanguarantee parameter (step 7802). A guarantee for the loan may bevalidated in response to the monitored social network information (step7804). A workflow may be enabled by which a human user enters the loanguarantee parameter to establish a social network data collection andmonitoring request (step 7808). In response to the validation of theloan, an action related to the loan may be undertaken automatically(step 7810). A robotic process automation circuit may be iterativelytrained to configure a data collection and monitoring action based on atleast one attribute of the loan, wherein the robotic process automationcircuit is trained on a training data set comprising at least one ofoutcomes from or human user interactions with the plurality ofalgorithms (step 7812). At least one domain to which the plurality ofalgorithms will apply may be determined (step 7814).

Referring to FIG. 79, an illustrative and non-limiting examplemonitoring system for validating conditions of a guarantee for a loan7900 is depicted. The example system may include a controller 79101. Thecontroller 79101 may include an Internet of Things data collection inputcircuit 7944, Internet of Things data collection circuit 7932, andseveral artificial intelligence circuits 7942 including a smart contractcircuit 7922, a guarantee validation circuit 7998, and a robotic processautomation circuit 7948.

The Internet of Things data collection input circuit 7944 may bestructured to interpret a loan guarantee parameter 7992. The Internet ofThings data collection circuit 7932 may be structured to collect datausing at least one algorithm that is configured to monitor Internet ofThings information collected from and about an entity 7964 involved in aloan 7930 in response to the loan guarantee parameter. The guaranteevalidation circuit 7998 structured to validate a guarantee for the loanin response to the monitored IoT information

The loan guarantee parameter 7992 may include a financial condition ofthe entity, wherein the entity is a guarantor for the loan. MonitoredIoT information includes at least one of a publicly stated valuation ofthe entity, a property owned by the entity as indicated by publicrecords, a valuation of a property owned by the entity, a bankruptcycondition of the entity, a foreclosure status of the entity, acontractual default status of the entity, a regulatory violation statusof the entity, a criminal status of an entity, an export controls statusof the entity, an embargo status of the entity, a tariff status of theentity, a tax status of the entity, a credit report of the entity, acredit rating of the entity, a web site rating of the entity, aplurality of customer reviews for a product of the entity, a socialnetwork rating of the entity, a plurality of credentials of the entity,a plurality of referrals of the entity, a plurality of testimonials forthe entity, a plurality of behaviors of the entity, a location of theentity, a jurisdiction of the entity, and a geolocation of the entity.

The loan may include at least one loan type selected from the loan typesconsisting of: an auto loan, an inventory loan, a capital equipmentloan, a bond for performance, a capital improvement loan, a buildingloan, a loan backed by an account receivable, an invoice financearrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The Internet of Things data collection circuit 7932 may be furtherstructured to obtain outcome data 7910, collateral data 7904 todetermine information about collateral condition 7911 for the loan,wherein the collateral comprises at least one item selected from theitems consisting of a vehicle, a ship, a plane, a building, a home, areal estate property, an undeveloped land, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property, and wherein the guarantee validation circuit 7998 isfurther structured to validate the guarantee of the loan in response tothe condition of the collateral for the loan.

Collateral condition 7911 may include a condition attribute selectedfrom the group consisting of a quality of the collateral, a status oftitle to the collateral, a status of possession of the collateral, astatus of a lien on the collateral, a new or used status, a type, acategory, a specification, a product feature set, a model, a brand, amanufacturer, a status, a context, a state, a value, a storage location,a geolocation, an age, a maintenance history, a usage history, anaccident history, a fault history, an ownership, an ownership history, aprice, an assessment, and a valuation.

The Internet of Things data collection input circuit 7944 may be furtherstructured to enable a workflow by which a human user enters the loanguarantee parameter 7992 to establish an Internet of Things datacollection request.

The smart contract circuit 7922 may be structured to automaticallyundertake an action related to the loan in response to the validation ofthe loan. The action related to the loan may be in response to the loanguarantee not being validated, and wherein the action comprises at leastone action selected from the actions consisting of: a foreclosureaction, a lien administration action, an interest-rate adjustmentaction, a default initiation action, a substitution of collateral, acalling of the loan, and providing an alert to second entity involved inthe loan.

The robotic process automation circuit 7948 may be structured to, basedon iteratively training on a training data set comprising human userinteractions with the Internet of Things data collection circuit,configure the loan guarantee parameter based on at least one attributeof the loan. The at least one attribute of the loan is obtained from asmart contract circuit that manage the loan. The training data set 7946may further include outcomes from a plurality of Internet of Things datacollection and monitoring requests performed by the Internet of Thingsdata collection circuit.

The robotic process automation circuit 7948 may be further structured todetermine at least one domain to which the Internet of Things datacollection circuit will apply.

Training may include training the robotic process automation circuit7948 to configure the at least one algorithm.

Referring to FIG. 80, an illustrative and non-limiting examplemonitoring method for validating conditions of a guarantee for a loan8000 is depicted. The example method may include interpreting a loanguarantee parameter (step 8002); collecting data using a plurality ofalgorithms that are configured to monitor Internet of Things (IoT)information collected from and about an entity involved in a loan inresponse to the loan guarantee parameter (step 8004); and validating aguarantee for the loan in response to the monitored IoT information(step 8005).

The loan guarantee parameter may be configured to obtain informationabout a financial condition of the entity, wherein the entity is aguarantor for the loan (step 8008). The at least one algorithm may beconfigured to obtain information about a condition of a collateral forthe loan (step 8010), wherein the collateral comprises at least one itemselected from the items consisting of a vehicle, a ship, a plane, abuilding, a home, a real estate property, an undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property; and validatingthe guarantee for the loan further in response to the condition of thecollateral for the loan.

A workflow by which a human user enters the loan guarantee parameter toestablish an Internet of Things data collection request may be enabled(step 8012).

An action related to the loan may be undertaken automatically inresponse to the validation (step 8014).

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a foreclosureaction.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a lienadministration action.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises an interest-rateadjustment action.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a defaultinitiation action.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a substitution ofcollateral.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises a calling of theloan.

The action related to the loan may be in response to the loan guaranteenot being validated, and wherein the action comprises providing an alertto a second entity involved in the loan.

A robotic process automation circuit may be iteratively trained toconfigure an Internet of Things data collection and monitoring actionbased on at least one attribute of the loan, wherein the robotic processautomation circuit is trained on a training data set comprising at leastone of outcomes from or human user interactions with the plurality ofalgorithms (step 8018).

At least one domain to which the at least one algorithm will apply maybe determined (step 8020). Training may include training the roboticprocess automation circuit to configure the plurality of algorithms.

The training data set may further include outcomes from a set of IoTdata collection and monitoring requests.

Referring now to FIG. 81, an illustrative and non-limiting examplerobotic process automation system for negotiating a loan 8100 isdepicted. The example system may include a controller 81101. Thecontroller 81101 may include a data collection circuit 8112, a valuationcircuit 8144, and several artificial intelligence circuits 8142including an automated loan classification circuit 8132, a roboticprocess automation circuit 8160, a smart contract circuit 8184, and aclustering circuit 8182.

The data collection circuit 8112 may be structured to collect collateraldata 8104 and create a training set of interactions 8110 from at leastone entity 8178 related to at least one loan transaction. An automatedloan classification circuit 8132 may be trained on the training set ofinteractions 1SCQ10 to classify a at least one loan negotiation action.The robotic process automation circuit 8160 may be trained on a trainingset of a plurality of loan negotiation actions 8174 classified by theautomated loan classification circuit 8132 and a plurality of loantransaction outcomes 8139 to negotiate a terms and conditions 8124 of anew loan 8130 on behalf of a party to the new loan.

The data collection circuit may further include at least one othersystem 8162 selected from the systems consisting of: an Internet ofThings system, a camera system, a networked monitoring system, aninternet monitoring system, a mobile device system, a wearable devicesystem, a user interface system, and an interactive crowdsourcingsystem. The at least one entity may be a party to the at least one loantransaction and may be selected from the entities consisting of: aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official,and an accountant.

The automated loan classification circuit 8132 may include a systemselected from the systems consisting of: a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizingmap, a fuzzy logic system, a random walk system, a random forest system,a probabilistic system, a Bayesian system, and a simulation system.

The robotic process automation circuit 8160 may be further trained on aplurality of interactions of parties with a plurality of user interfaces8172 involved in a plurality of lending processes.

The smart contract circuit 8184 may be structured to automaticallyconfigure a smart contract 8188 for the new loan 8130 based on anoutcome of the negotiation.

A distributed ledger 8180 may be associated with the new loan 8130,wherein the distributed ledger 8180 is structured to record at least oneof an outcome and a negotiating event of the negotiation.

The new loan may include at least one loan type selected from the loantypes consisting of: an auto loan, an inventory loan, a capitalequipment loan, a bond for performance, a capital improvement loan, abuilding loan, a loan backed by an account receivable, an invoicefinance arrangement, a factoring arrangement, a pay day loan, a refundanticipation loan, a student loan, a syndicated loan, a title loan, ahome loan, a venture debt loan, a loan of intellectual property, a loanof a contractual claim, a working capital loan, a small business loan, afarm loan, a municipal bond, and a subsidized loan.

The valuation circuit 8144 may be structured to use a valuation model8152 to determine a value for a collateral for the new loan. Thecollateral may include at least one item selected from the itemsconsisting of: a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, an itemof equipment, a tool, an item of machinery, and an item of personalproperty.

The valuation circuit may further include a market value data collectioncircuit 8148 structured to monitor and report on marketplace informationrelevant to a value of the collateral. The market value data collectioncircuit 8148 may monitor pricing or financial data for an offsetcollateral item 8134 in at least one public marketplace. A set of offsetcollateral items 8134 for valuing the collateral may be constructedusing a clustering circuit 8182 based on an attribute of the collateral.The attribute may be selected from among a category of the collateral,an age of the collateral, a condition of the collateral 8111, a historyof the collateral, a storage condition of the collateral, and ageolocation of the collateral. The terms and conditions 8124 for the newloan may include at least one member selected from the group consistingof: a principal amount of debt, a balance of debt, a fixed interestrate, a variable interest rate, a payment amount, a payment schedule, aballoon payment schedule, a specification of collateral, a specificationof substitutability of collateral, a party, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, and a consequence of default.

Referring now to FIG. 82, an illustrative and non-limiting examplerobotic process automation method 8200 for negotiating a loan 8100 isdepicted. The example method may include collecting a training set ofinteractions from at least one entity related to at least one loantransaction (step 8202); training an automated loan classificationcircuit on the training set of interactions to classify a at least oneloan negotiation action (step 8204); and training a robotic processautomation circuit on a training set of a plurality of loan negotiationactions classified by the automated loan classification circuit and aplurality of loan transaction outcomes to negotiate a terms andconditions of a new loan on behalf of a party to the new loan (step8208).

The robotic process automation circuit may be trained on a plurality ofinteractions of parties with a plurality of user interfaces involved ina plurality of lending processes (step 8210).

A smart contract for the new loan may be configured based on an outcomeof the negotiation (step 8212).

At least one of an outcome and a negotiating event of the negotiationmay be recorded in a distributed ledger associated with the new loan(step 8214).

A value for a collateral for the new loan may be determined using avaluation model (step 8218).

An example method may further include monitoring and reporting onmarketplace information relevant to a value of the collateral (step8220).

A set of offset collateral items for valuing the collateral may beconstructed using a similarity clustering algorithm based on anattribute of the collateral (step 8222).

Referring to FIG. 83, an illustrative and non-limiting example systemfor system for adaptive intelligence and robotic process automationcapabilities 8300 is depicted. The example system may include a datacollection circuit 8306 which may collect data such loan collectionoutcomes 8303, training set of loan interactions 8304 which may includecollection of payments 8305 and the like. The data may be collected fromloan transactions 8319, loan data 8301, and data regarding entities 8302associated with the loan, and the like. The data may be collected from avariety of sources and systems such as: an Internet of Things system, acamera system, a networked monitoring system, an internet monitoringsystem, a mobile device system, a wearable device system, a userinterface system, and an interactive crowdsourcing system. The loancollection outcomes 8303 may include at least outcome such a response toa collection contact event, a payment of a loan, a default of a borroweron a loan, a bankruptcy of a borrower of a loan, an outcome of acollection litigation, a financial yield of a set of collection actions,a return on investment on collection, a measure of reputation of a partyinvolved in collection, and the like.

The system may also include an artificial intelligence circuit 8310 thatmay be structured to classify a set of loan collection actions 8309based at least in part on the training set of loan interactions 8304.The artificial intelligence circuit 8310 may include at least one systemsuch as a machine learning system, a model-based system, a rule-basedsystem, a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and the like.

The system may also include a robotic process automation circuit 8313structured to perform at least one loan collection action 8311 on behalfof a party to a loan 8312 based at least in part on the training set ofloan interactions 8304 and the set of loan collection outcomes 8303. Theloan collection action 8311 undertaken by the robotic process automationcircuit 8313 may be at least one of a referral of a loan to an agent forcollection, configuration of a collection communication, scheduling of acollection communication, configuration of content for a collectioncommunication, configuration of an offer to settle a loan, terminationof a collection action, deferral of a collection action, configurationof an offer for an alternative payment schedule, initiation of alitigation, initiation of a foreclosure, initiation of a bankruptcyprocess, a repossession process, placement of a lien on collateral, andthe like. The party to a loan 8312 may include least one such as aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official,an accountant, and the like. Loans may include at least one auto loan,an inventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, a subsidizedloan and the like.

The system may further include an interface circuit 8308 structured toreceive interactions 8307 from one or more of the loan entities 8302. Insome embodiments the robotic process automation circuit 8313 may betrained on the interactions 8307. The system may further include a smartcontract circuit 8318 structured to determine completion of anegotiation of the loan collection action 8311 and modify a contract8316 based on an outcome of the negation 8317.

The system may further include a distributed ledger circuit 8315structured to determine at least one of a collection outcome 8320 or anevent 8321 associated with the loan collection action 8311. Thedistributed ledger circuit 8315 may be structured to record, in adistributed ledger 8314 associated with the loan, the event 8321 and/orthe collection outcome 8320.

Referring to FIG. 84, an illustrative and non-limiting example method8400 is depicted. The example method 8400 may include step 8401 forcollecting a training set of loan interactions and a set of loancollection outcomes among entities for a set of loan transactions,wherein the training set of loan interactions comprises a collection ofa set of payments for a set of loans. A set of loan collection actionsbased at least in part the training set of loan interactions may beclassified (step 8402). The method may further include the step 8403 ofspecifying a loan collection action on behalf of a party to a loan basedat least in part on the training set of loan interactions and the set ofloan collection outcomes.

The method 8400 may further include the step 8404 of determiningcompletion of a negotiation of the loan collection action. Based on theoutcome of the negotiations a smart contract may be modified in step8405. The method may also include the step 8406 of determining at leastone of a collection outcome or an event associated with the loancollection action. The at least one of the collection outcome or theevent may be recorded in a distributed ledger associate with the loan instep 8407.

Referring to FIG. 85, an illustrative and non-limiting example systemfor system for adaptive intelligence and robotic process automationcapabilities 8500 is depicted. The example system may include a datacollection circuit 8506 structured to collect a training set of loaninteractions between entities 8502, wherein the training set of loaninteractions may include a set of loan refinancing activities 8503 and aset of loan refinancing outcomes 8504. The system may include anartificial intelligence circuit 8310 structured to classify the set ofloan refinancing activities, wherein the artificial intelligence circuitis trained on the training set of loan interactions. The system mayinclude a robotic process automation circuit 8513 structured to performa second loan refinancing activity 8511 on behalf of a party to a secondloan 8312, wherein the robotic process automation circuit is trained onthe set of loan refinancing activities and the set of loan refinancingoutcomes. The example system may include a data collection circuit 8506which may collect data such as a training set of loan interactionsbetween entities 8502. Data related to the set of loan interactionsbetween entities 8502 may include data related to loan refinancingactivities 8503 and loan refinancing outcomes 8504. The data may becollected from loan data 8501, information about entities 8502, and thelike. The data may be collected from a variety of sources and systemssuch as: an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, and aninteractive crowdsourcing system. The loan refinancing activity 8503 mayinclude at least one activity such as initiating an offer to refinance,initiating a request to refinance, configuring a refinancing interestrate, configuring a refinancing payment schedule, configuring arefinancing balance, configuring collateral for a refinancing, managinguse of proceeds of a refinancing, removing or placing a lien associatedwith a refinancing, verifying title for a refinancing, managing aninspection process, populating an application, negotiating terms andconditions for a refinancing, closing a refinancing, and the like.

The system may also include an artificial intelligence circuit 8310 thatmay be structured to classify the set of loan refinancing activities8503 based at least in part on the training set of loan interactions8505. The artificial intelligence circuit 8310 may include at least onesystem such as a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network a self-organizing map, a fuzzy logic system, arandom walk system, a random forest system, a probabilistic system, aBayesian system, a simulation system, and the like.

The system may also include a robotic process automation circuit 8513structured to perform a second loan refinancing activity 8511 on behalfof a party to a second loan 8312 based at least in part on the set ofloan refinancing activities 8503 and the set of loan refinancingoutcomes 8504. The party to a second loan 8312 may include least onesuch as a primary lender, a secondary lender, a lending syndicate, acorporate lender, a government lender, a bank lender, a secured lender,bond issuer, a bond purchaser, an unsecured lender, a guarantor, aprovider of security, a borrower, a debtor, an underwriter, aninspector, an assessor, an auditor, a valuation professional, agovernment official, an accountant, and the like.

The second loan 8519 may include at least one auto loan, an inventoryloan, a capital equipment loan, a bond for performance, a capitalimprovement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, a subsidizedloan and the like.

The system may further include an interface circuit 8508 structured toreceive interactions 8507 from one or more of the entities 8502. In someembodiments the robotic process automation circuit 8513 may be trainedon the interactions 8507. The system may further include a smartcontract circuit 8518 structured to determine completion of the secondloan refinancing activity 8511 and modify a smart refinance contract8517 based on an outcome of the second loan refinancing activity 8511.

The system may further include a distributed ledger circuit 8315structured to determine an event 8321 associated with the second loanrefinancing activity 8511. The distributed ledger circuit 8315 may bestructured to record, in a distributed ledger 8314 associated with thesecond loan 8519, the event 8321 associated with the second loanrefinancing activity 8511.

Referring to FIG. 86, an illustrative and non-limiting example method8600 is depicted. The example method 8600 may include step 8601 forcollecting a training set of loan interactions between entities, whereinthe training set of loan interactions comprises a set of loanrefinancing activities and a set of loan refinancing outcomes. A set ofloan refinancing activities based at least in part the training set ofloan interactions may be classified (step 8602). The method may furtherinclude the step 8603 of specifying a second loan refinancing activityon behalf of a party to a second loan based at least in part on the setof loan refinancing activities and the set of loan refinancing outcomes.

The method 8600 may further include the step 8604 of determiningcompletion of the second loan refinancing activity. Based on the outcomeof the second loan refinancing activity a smart refinance contract maybe modified in step 8605. The method may also include the step 8606 ofdetermining an event associated with the second loan refinancingactivity. The event associated with the second loan refinancing activitymay be recorded in a distributed ledger associate with the second loanin step 8607.

Referring to FIG. 87, an illustrative and non-limiting example systemfor system for adaptive intelligence and robotic process automationcapabilities 8700 is depicted. The example system may include a datacollection circuit 8705 which may collect data such as a training set ofloan interactions 8704 between entities which may include a set of loanconsolidation transactions 8703 and the like. The data may be collectedfrom loans 8701, information re. entities 8702, and the like. The datamay be collected from a variety of sources and systems such as: anInternet of Things system, a camera system, a networked monitoringsystem, an internet monitoring system, a mobile device system, awearable device system, a user interface system, and a crowdsourcingsystem.

The system may also include an artificial intelligence circuit 8310 thatmay be structured to classify a set of loans as candidates forconsolidation based at least in part on the training set of loaninteractions 8704. The artificial intelligence circuit 8310 may includeat least one system such as a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, a simulation system, and the like.

The system may also include a robotic process automation circuit 8713structured to manage a consolidation of at least a subset of the set ofloans 8711 on behalf of a party to the loan consolidation 8712 based atleast in part on the training set of loan consolidation transactions8703. Managing the consolidation may include identification of loansfrom a set of candidate loans, preparation of a consolidation offer,preparation of a consolidation plan, preparation of contentcommunicating a consolidation offer, scheduling a consolidation offer,communicating a consolidation offer, negotiating a modification of aconsolidation offer, preparing a consolidation agreement, executing aconsolidation agreement, modifying collateral for a set of loans,handling an application workflow for consolidation, managing aninspection, managing an assessment, setting an interest rate, deferringa payment requirement, setting a payment schedule, or closing aconsolidation agreement.

The artificial intelligence circuit may further include a model that maybe used to classify loans are candidates for consolidation. The modelmay process attributes of entities, the attributes may include identityof a party, interest rate, payment balance, payment terms, paymentschedule, type of loan, type of collateral, financial condition ofparty, payment status, condition of collateral, value of collateral, andthe like.

The party to a loan consolidation 8712 may include least one such as aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official,an accountant, and the like.

Loans 8701 may include at least one auto loan, an inventory loan, acapital equipment loan, a bond for performance, a capital improvementloan, a building loan, a loan backed by an account receivable, aninvoice finance arrangement, a factoring arrangement, a pay day loan, arefund anticipation loan, a student loan, a syndicated loan, a titleloan, a home loan, a venture debt loan, a loan of intellectual property,a loan of a contractual claim, a working capital loan, a small businessloan, a farm loan, a municipal bond, a subsidized loan and the like.

The system may further include an interface circuit 8707 structured toreceive interactions 8706 from one or more of the entities 8702. In someembodiments the robotic process automation circuit 8713 may be trainedon the interactions 8706. The system may further include a smartcontract circuit 8720 structured to determine a completion of anegotiations of the consolidation and modify a contract 8718 based on anoutcome of the negotiation 8719.

The system may further include a distributed ledger circuit 8717structured to determine at least one of a collection outcome 8715 or anegotiation event 8716 associated with the consolidation. Thedistributed ledger circuit 8717 may be structured to record, in adistributed ledger 8714 associated with the loan, the negotiation event8716 and/or the collection outcome 8715.

Referring to FIG. 88, an illustrative and non-limiting example method8800 is depicted. The example method 8800 may include step 8801collecting a training set of loan interactions between entities, whereinthe training set of loan interactions comprises a set of loanconsolidation transactions. A set of loans as candidates forconsolidation based at least in part on the training set of loaninteractions may be classified (step 8802). The method may furtherinclude the step 8803 of managing a consolidation of at least a subsetof the set of loans on behalf of a party to the consolidation based atleast in part on the set of loan consolidation transactions.

The method 8800 may further include the step 8804 of determiningcompletion of a negotiation of the consolidation of at least one loanfrom the subset of the set of loans. Based on the outcome of thenegotiations a smart contract may be modified in step 8805. The methodmay also include the step 8806 of determining at least one of an outcomeand a negotiation event associated with the consolidation of at leastthe subset of the set of loans. The at least one of the outcome and thenegotiation event may be recorded in a distributed ledger associate withthe consolidation in step 8807.

Referring to FIG. 89, an illustrative and non-limiting example systemfor system for adaptive intelligence and robotic process automationcapabilities 8900 is depicted. The example system may include a datacollection circuit 8905 which may collect data information aboutentities 8902 involved in a set of factoring loans 8901 and a trainingset of interactions 8904 between entities for a set of factoring loantransactions 8903. The data may be collected from a variety of sourcesand systems such as: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and acrowdsourcing system.

The system may also include an artificial intelligence circuit 8911 thatmay be structured to classify entities 8908 involved in the set offactoring loans based at least in part on the training set ofinteractions 8904. The artificial intelligence circuit 8911 may includeat least one system such as a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, a simulation system, and the like.

The system may also include a robotic process automation circuit 8913structured to manage a factoring loan 8912 based at least in part on thefactoring loan transactions 8903. Managing the factoring loan mayinclude managing at least one of a set of assets for factoring,identification of loans for factoring from a set of candidate loans,preparation of a factoring offer, preparation of a factoring plan,preparation of content comn1tmicating a factoring offer, scheduling afactoring offer, communicating a factoring offer, negotiating amodification of a factoring offer, preparing a factoring agreement,executing a factoring agreement, modifying collateral for a set offactoring loans, handing transfer of a set of accounts receivable,handling an application workflow for factoring, managing an inspection,managing an assessment of a set of assets to be factored, setting aninterest rate, deferring a payment requirement, setting a paymentschedule, or dosing a factoring agreement.

The artificial intelligence circuit 8911 may further include a model8909 that may be used to process attributes of entities involved in theset of factoring loans, the attributes may include assets used forfactoring, identity of a party, interest rate, payment balance, paymentterms, payment schedule, type of loan, type of collateral, financialcondition of party, payment status, condition of collateral, or value ofcollateral. The assets used for factoring may include a set of accountsreceivable 8910. At least one entity of the entities 8902 may be a partyto at least one factoring loan transactions 8903. The party may includeleast one such as a primary lender, a secondary lender, a lendingsyndicate, a corporate lender, a government lender, a bank lender, asecured lender, bond issuer, a bond purchaser, an unsecured lender, aguarantor, a provider of security, a borrower, a debtor, an underwriter,an inspector, an assessor, an auditor, a valuation professional, agovernment official, an accountant, and the like.

The system may further include an interface circuit 8907 structured toreceive interactions 8906 from one or more of the entities 8902. In someembodiments the robotic process automation circuit 8913 may be trainedon the interactions 8906.

The system may further include a smart contract circuit 8920 structuredto determine a completion of a negotiations of the factoring loan andmodify a contract 8918 based on an outcome of the negotiation 8919.

The system may further include a distributed ledger circuit 8917structured to determine at least one of an outcome 8915 or a negotiationevent 8916 associated with the negotiation of the factoring loan. Thedistributed ledger circuit 8917 may be structured to record, in adistributed ledger 8914 associated with the factoring loan, thenegotiation event 8916 and/or the outcome 8915.

Referring to FIG. 90, an illustrative and non-limiting example method9000 is depicted. The example method 9000 may include step 9001collecting information about entities involved in a set of factoringloans and a training set of interactions between entities for a set offactoring loan transactions. Entities involved in the set of factoringloans may be classified based at least in part on the training set ofloan interactions step 9002. The method may further include the step9003 of managing a factoring loan based at least in part on the set offactoring loan interactions.

The method 9000 may further include the step 9004 of determiningcompletion of a negotiation of the factoring loan. Based on the outcomeof the negotiations a smart contract may be modified in step 9005. Themethod may also include the step 9006 of determining at least one of anoutcome and a negotiation event associated with the negotiation of thefactoring loan. The at least one of the outcome and the negotiationevent may be recorded in a distributed ledger associate with thefactoring loan in step 9007.

Referring to FIG. 91, an illustrative and non-limiting example systemfor system for adaptive intelligence and robotic process automationcapabilities 9100 is depicted. The example system may include a datacollection circuit 9106 which may collect data information aboutmortgage loans 9101 and entities 9102 involved in the set of mortgageloans and mortgage loan activities 9105 and a training set ofinteractions 9104 between entities for a set of mortgage loantransactions 9103. The data may be collected from a variety of sourcesand systems such as: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and acrowdsourcing system.

The system may also include an artificial intelligence circuit 9110 thatmay be structured to classify entities 9109 involved in the set ofmortgage loan activities based at least in part on the training set ofinteractions 9104. The artificial intelligence circuit 9110 may includeat least one system such as a machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, a simulation system, and the like.

The system may also include a robotic process automation circuit 9112structured to broker a mortgage loan 9111 based at least in part on atleast one of the set of mortgage loan activities 9105 and the trainingset of interactions 9104. The set of mortgage loan activities 9105and/or the set of mortgage loan transactions 9103 may include activitiesselected from a group consisting of: among marketing activity,identification of a set of prospective borrowers, identification ofproperty, identification of collateral, qualification of borrower, titlesearch, title verification, property assessment, property inspection,property valuation, income verification, borrower demographic analysis,identification of capital providers, determination of available interestrates, determination of available payment terms and conditions, analysisof existing mortgage, comparative analysis of existing and new mortgageterms, completion of application workflow, population of fields ofapplication, preparation of mortgage agreement, completion of scheduleto mortgage agreement, negotiation of mortgage terms and conditions withcapital provider, negotiation of mortgage terms and conditions withborrower, transfer of title, placement of lien, or closing of mortgageagreement.

The artificial intelligence circuit 9110 may further include a modelthat may be used to process attributes of entities involved in the setof mortgage loan activities, the attributes may properties that aresubject to mortgages, assets used for collateral, identity of a party,interest rate, payment balance, payment terms, payment schedule, type ofmortgage, type of property, financial condition of party, paymentstatus, condition of property, or value of property. In embodiments,brokering the mortgage loan comprises at least one activity such asmanaging at least one of a property that is subject to a mortgage,identification of candidate mortgages from a set of borrower situations,preparation of a mortgage offer, preparation of content communicating amortgage offer, scheduling a mortgage offer, communicating a mortgageoffer, negotiating a modification of a mortgage offer, preparing amortgage agreement, executing a mortgage agreement, modifying collateralfor a set of mortgage loans, handing transfer of a lien, handling anapplication workflow, managing an inspection, managing an assessment ofa set of assets to be subject to a mortgage, setting an interest rate,deferring a payment requirement, setting a payment schedule, closing amortgage agreement, and the like

In embodiments at least one entity of the entities 9102 may be a partyto at least one mortgage loan transactions of the set of mortgage loantransactions 9103. The party may include least one such as a primarylender, a secondary lender, a lending syndicate, a corporate lender, agovernment lender, a bank lender, a secured lender, bond issuer, a bondpurchaser, an unsecured lender, a guarantor, a provider of security, aborrower, a debtor, an underwriter, an inspector, an assessor, anauditor, a valuation professional, a government official, an accountant,and the like.

The system may further include an interface circuit 9108 structured toreceive interactions 9107 from one or more of the entities 9102. In someembodiments the robotic process automation circuit 9112 may be trainedon the interactions 9107.

The system may further include a smart contract circuit 9119 structuredto determine a completion of a negotiations of the mortgage loan andmodify a smart contract 9117 based on an outcome of the negotiation9118.

The system may further include a distributed ledger circuit 9116structured to determine at least one of an outcome 9114 or a negotiationevent 9115 associated with the negotiation of the mortgage loan. Thedistributed ledger circuit 9116 may be structured to record, in adistributed ledger 9113 associated with the mortgage loan, thenegotiation event 9115 and/or the outcome 9114.

Referring to FIG. 92, an illustrative and non-limiting example method9200 is depicted. The example method 9200 may include step 9201collecting information about entities involved in a set of mortgage loanactivities and a training set of interactions between entities for a setof mortgage loan transactions. Entities involved in the set of factoringloans may be classified based at least in part on the training set ofloan interactions step 9202. The method may further include the step9203 of brokering a mortgage loan based at least in part on at least oneof the set of mortgage loan activities and the training set ofinteractions.

The method 9200 may further include the step 9204 of determiningcompletion of a negotiation of the mortgage loan. Based on the outcomeof the negotiations a smart contract may be modified in step 9205. Themethod may also include the step 9206 of determining at least one of anoutcome and a negotiation event associated with the negotiation of themortgage loan. The at least one of the outcome and the negotiation eventmay be recorded in a distributed ledger associate with the mortgage loanin step 9207.

Referring to FIG. 93, an illustrative and non-limiting example systemfor system for adaptive intelligence and robotic process automationcapabilities 9300 is depicted. The example system may include a datacollection circuit 9308 which may collect data about entities 9305involved in a set of debt transactions 9301, training data set ofoutcomes 9306 related to the entities, and a training set of debtmanagement activities 9307. The data may be collected from a variety ofsources and systems such as: Internet of Things devices, a set ofenvironmental condition sensors, a set of crowdsourcing services, a setof social network analytic services, or a set of algorithms for queryingnetwork domains, and the like.

The system may also include a condition classifying circuit 9314 thatmay be structured to classify a condition 9311 of at least one entity ofthe entities 9305. The condition classifying circuit 9314 may include amodel 9312 and a set of artificial intelligence circuits 9313. The model9312 may be trained using the training data set of outcomes 9306 relatedto the entities. The artificial intelligence circuits 9313 may includeat least one system such as machine learning system, a model-basedsystem, a rule-based system, a deep learning system, a hybrid system, aneural network, a convolutional neural network, a feed forward neuralnetwork, a feedback neural network, a self-organizing map, a fuzzy logicsystem, a random walk system, a random forest system, a probabilisticsystem, a Bayesian system, or a simulation system.

The system may also include an automated debt management circuit 9316structured to manage an action related to a debt 9315. The automateddebt management circuit 9316 may be trained on the training set of debtmanagement activities 9307.

In embodiments, at least one debt transaction of the set of debttransactions 9301 may be include an auto loan, an inventory loan, acapital equipment loan, a bond for performance, a capital improvementloan, a building loan, a loan backed by an account receivable, aninvoice finance arrangement, a factoring arrangement, a pay day loan, arefund anticipation loan, a student loan, a syndicated loan, a titleloan, a home loan, a venture debt loan, a loan of intellectual property,a loan of a contractual claim, a working capital loan, a small businessloan, a farm loan, a municipal bond, a subsidized loan, and the like.

In embodiments, the entities 9305 involved in the set of debttransactions may include at least one of set of parties 9302 and a setof assets 9304. The assets 9304 may include a municipal asset, avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, or an item of personal property. The system mayfurther include a set of sensors 9303 positioned on at least one asset9304 from the set of assets, on a container for least one asset from theset of assets, and on a package for at least one asset from the set ofassets, wherein the set of sensors configured to associate sensorinformation sensed by the set of sensors with a unique identifier forthe at least one asset from the set of assets. The sensors 9303 mayinclude image, temperature, pressure, humidity, velocity, acceleration,rotational, torque, weight, chemical, magnetic field, electrical field,or position sensors.

In embodiments, the system may further include a set of block chaincircuits 9324 structured to receive information from the data collectioncircuit 9308 and the set of sensors 9303 and storing the information ina blockchain 9326. The access to the blockchain 9326 may be provided viaa secure access control interface circuit 9323.

An automated agent circuit 9325 may be structured to process eventsrelevant to at least one of a value, a condition, and an ownership of atleast one asset of the set of assets and further structured to undertakea set of actions related to a debt transaction to which the asset isrelated.

The system may further include an interface circuit 9310 structured toreceive interactions 9309 from at least one of the entities 9305. Inembodiments the automated debt management circuit 9316 may be trained onthe interactions 9309. In some embodiments the system may furtherinclude a market value data collection circuit 9318 structured tomonitor and report marketplace information 9317 relevant to a value of aof at least one asset of a set of assets 9304. The market value datacollection circuit 9318 may be further structured to monitor at leastone pricing and financial data for items that are similar to at leastone asset in the set of assets in at least one public marketplace. A setof similar items for valuing at least one asset from the set of assetsmay be constructed using a similarity clustering algorithm based onattributes of the assets. In embodiments, at least one attribute of theattributes of the assets may include a category of assets, asset age,asset condition, asset history, asset storage, geolocation of assets,and the like.

In embodiments, the system may further include a smart contract circuit9322 structured to manage a smart contract 9319 for a debt transaction9321. The smart contract circuit 9322 may be further structured toestablish a set of terms and conditions 9320 for the debt transaction9321. At least one of the terms and conditions may include a principalamount of debt, a balance of debt, a fixed interest rate, a variableinterest rate, a payment amount, a payment schedule, a balloon paymentschedule, a specification of collateral, a specification ofsubstitutability of collateral, a party, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, a consequence of default, andthe like.

In embodiments at least one action related to a debt 9315 may includeoffering a debt transaction, underwriting a debt transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating title, managing inspection, recording a change intitle, assessing the value of an asset, calling a loan, closing atransaction, setting tem1s and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating debt, or consolidating debt. At least one debt managementactivity from the training set of debt management activities 9307 mayinclude offering a debt transaction, underwriting a debt transaction,setting an interest rate, deferring a payment requirement, modifying aninterest rate, validating title, managing inspection, recording a changein title, assessing a value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating debt, or consolidating debt.

Referring to FIG. 94, an illustrative and non-limiting example method9400 is depicted. The example method 9400 may include step 9401collecting information about entities involved in a set of debttransactions, training data set of outcomes related to the entities, anda training set of debt management activities. The example method mayfurther include classifying a condition of at least one entity of theentities based at least in part the training data set of outcomesrelated to the entities (step 9402). The example method may furtherinclude managing an action related to a debt based at least in part onthe training set of debt management activities (step 9403). The examplemethod may further include receiving information from a set of sensorspositioned on at least one asset (step 9404). The example method mayfurther include storing the information in a blockchain, wherein accessto the blockchain is provided via a secure access control interface fora party for a debt transaction involving the at least one asset from theset of assets (step 9405). In step 9406 the method may includeprocessing events relevant to at least one of a value, a condition, oran ownership of at least one asset of the set of assets. In step 9407the method may include processing a set of actions related to a debttransaction to which the asset is related. In embodiments the method mayfurther include receiving interactions from at least one of the entities(step 9408), monitoring and reporting marketplace information relevantto a value of a of at least one asset of a set of assets (step 9409),constructing using a similarity clustering algorithm based on attributesof the assets a set of similar items for valuing at least one asset fromthe set of assets (step 9410), managing a smart contract for a debttransaction (step 9411) and establishing a set of terms and conditionsfor the smart contract for the debt transaction (step 9412).

Referring to FIG. 95, an illustrative and non-limiting example systemfor system for adaptive intelligence and robotic process automationcapabilities 9500 is depicted.

The example system may include a crowdsourcing data collection circuit9505 structured to collect information about entities 9503 involved in aset of bond transactions 9502 and a training data set of outcomesrelated to the entities 9503. The system may further include a conditionclassifying circuit 9511 structured to classify a condition of a set ofissuers 9508 using the information from the crowdsourcing datacollection circuit 9505 and a model 9509. The condition classifyingcircuit 9511 may include artificial intelligence circuits 9510. Themodel 9509 may be trained using the training data set of outcomes 9504related to the set of issuers. The example system may further include anautomated agent circuit 9519 structured to perform an action related toa debt transaction in response to the classified condition of at leastone issuer of the set of issuers. In embodiments at least one entity9503 may include a set of issuers, a set of bonds, a set of parties, ora set of assets. At least one issuer may include a municipality, acorporation, a contractor, a government entity, a non-governmentalentity, or a non-profit entity. At least one bond may include amunicipal bond, a government bond, a treasury bond, an asset-backedbond, or a corporate bond.

In embodiments, the condition classified 9508 by the conditionclassifying circuit 9511 may include a default condition, a foreclosurecondition, a condition indicating violation of a covenant, a financialrisk condition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition, an entity health condition,or the like. The crowdsourcing data collection circuit 9505 may bestructured to enable a user interface 9507 by which a user may configurea crowdsourcing request 9506 for information relevant to the conditionabout the set of issuers.

The system may further include a configurable data collection andmonitoring circuit 9513 structured to monitor at least one issuer fromthe set of issuers 9512. The configurable data collection and monitoringcircuit 9513 may include a system such as: Internet of Things devices, aset of environmental condition sensors, a set of social network analyticservices, or a set of algorithms for querying network domains. Theconfigurable data collection and monitoring circuit 9513 mat bestructured to monitor an at least one environment such as: a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, or a vehicle.

In embodiments a set of bonds associated with the set of bondtransactions 9502 may be backed by a set of assets 9501. At least oneasset 9501 may include a municipal asset, a vehicle, a ship, a plane, abuilding, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, an item of personal property, or the like.

In embodiments, the system may further include an automated agentcircuit 9519 structured to processes events relevant to at least one ofa value, a condition, or an ownership of at least one asset of the atleast one issuer of the set of issuers, and to perform the actionrelated to the debt transaction in response to at least one of theprocessed events.

The action 9518 may include offering a debt transaction, underwriting adebt transaction, setting an interest rate, deferring a paymentrequirement, modifying an interest rate, validating title, managinginspection, recording a change in title, assessing the value of anasset, calling a loan, closing a transaction, setting terms andconditions for a transaction, providing notices required to be provided,foreclosing on a set of assets, modifying terms and conditions, settinga rating for an entity, syndicating debt, consolidating debt, and thelike. The condition classifying circuit 9511 may include a system suchas: a machine learning system, a model-based system, a rule-basedsystem, a deep learning system, a hybrid system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, or a simulation system.

In embodiments the system may further include an automated bondmanagement circuit 9527 configured to manage an action related to thebond 9524 related to the at least one issuer of the set of issuers. Theautomated bond management circuit 9527 may be trained on a training setof bond management activities 9526. The automated bond managementcircuit 9527 may be further trained on a set of interactions of parties9525 with a set of user interfaces involved in a set of bond transactionactivities. At least one bond transaction may include a debttransaction, underwriting a debt transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating debt,consolidating debt, or the like.

In embodiments the system may further include a market value datacollection circuit 9517 structured to monitor and reports on marketplaceinformation 9514 relevant to a value of at least one of the issuer or aset of assets. Reporting may include reporting on: a municipal asset, avehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, or an item of personal property. The market valuedata collection circuit 9517 may be structured to monitor pricing 9516or financial data 9515 for items that are similar to the assets in atleast one public marketplace. The market value data collection circuit9517 may be further structured to construct a set of similar items forvaluing the assets using a similarity clustering algorithm based onattributes of the assets. At least one attribute from the attributes maybe selected from: a category of the assets, asset age, asset condition,asset history, asset storage, or geolocation of assets.

In embodiments, the system may further include a smart contract circuit9523 structured for managing a smart contract 9520 for a bondtransaction 9522 in response to the classified condition of the at leastone issuer of the set of issuers. The smart contract circuit 9523 may bestructured to determine terms and conditions 9521 for the bond. At leastone term and condition 9521 may include a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of assets that back the bond, a specification ofsubstitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, aconsequence of default, and the like.

Referring to FIG. 96, an illustrative and non-limiting example method9600 is depicted. The example method 9600 may include step 9601 ofcollecting information about entities involved in a set of bondtransactions of a set of bonds and a training data set of outcomesrelated to the entities. The method may further include the step 9602 ofclassifying a condition of a set of issuers using the collectedinformation and a model, wherein the model is trained using the trainingdata set of outcomes related to the set of issuers. The method mayfurther include processing events relevant to at least one of a value, acondition, or an ownership of at least one asset of the set of assets(step 9603). The method may further include the steps of: performing anaction 9604 related to a debt transaction to which the asset is related,managing an action 9605 related to the bond based at least in part atraining set of bond management activities, monitoring and reporting onmarketplace information 9606 relevant to a value of at least one of theissuer and a set of assets, managing a smart contract 9607 for a bondtransaction, and determining terms and conditions 9608 for the smartcontract for at least one bond.

Referring now to FIG. 97, an illustrative and non-limiting examplesystem for monitoring a condition of an issuer for a bond 9700 isdepicted The example system may include a controller 9701 The controller9701 may include a data collection circuit 9712, a market value datacollection circuit 9756, a social networking input circuit 9744, asocial network data collection circuit 9732, and several artificialintelligence circuits 9742 including a smart contract circuit 9722, anautomated bond management circuit 9750, a condition classifying circuit9748, a clustering circuit 9762, and an event processing circuit 9752.

The social network data collection circuit 9732 may be structured tocollect social network information 9710 about at least one entity 9764involved in at least one transaction 9730 comprising at least one bond;and a condition classifying circuit 9748 may be structured to classify acondition of the at least one entity in accordance with a model 9774 andbased on information from the social network data collection circuit,wherein the model is trained using a training data set 9754 9746 of aplurality of outcomes related to the at least one entity. The at leastone entity may be selected from the entities consisting of: a bondissuer, a bond, a party, and an asset. The bond issuer may be selectedfrom the bond issuers consisting of a municipality, a corporation, acontractor, a government entity, a non-governmental entity, and anon-profit entity. The bond may be selected from the entities consistingof a municipal bond, a government bond, a treasury bond, an asset-backedbond, and a corporate bond.

The condition classified by the condition classifying circuit 9748 maybe at least one of a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition or an entity healthcondition.

The social network data collection circuit 9732 may further include asocial networking input circuit 9744 which may be structured to receiveinput from a user used to configure a query for information about the atleast one entity.

The data collection circuit 9712 may be structured to monitor at leastone of an Internet of Things device, an environmental condition sensor,a crowdsourcing request circuit, a crowdsourcing communication circuit,a crowdsourcing publishing circuit, and an algorithm for queryingnetwork domains associated with the monitored items 9711.

The data collection circuit 9712 may be further structured to monitor anenvironment selected from the group consisting of: a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle associated with the monitoreditems 9711.

The at least one bond is backed by at least one asset. The at least oneasset may be selected from the assets consisting of: a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

The event processing circuit 9752 may be structured to process an eventrelevant to at least one of a value, a condition, and an ownership ofthe at least one asset and undertake an action related to the at leastone transaction. The action may be selected from the actions consistingof: a bond transaction, underwriting a bond transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating title, managing inspection, recording a change intitle, assessing the value of an asset, calling a loan, closing atransaction, setting tem1s and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating bonds, and consolidating bonds.

The condition classifying circuit 9748 may further include a systemselected from the systems consisting of: a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizingmap, a fuzzy logic system, a random walk system, a random forest system,a probabilistic system, a Bayesian system, and a simulation system.

The automated bond management circuit 9750 may be structured to managean action related to the at least one bond, wherein the automated bondmanagement circuit is trained on a training data set of a plurality ofbond management activities.

The automated bond management circuit 9750 may be trained on a trainingset 9754 comprising a plurality of interactions of parties with aplurality of user interfaces involved in a plurality of bond transactionactivities. The plurality of bond transaction activities may be selectedfrom the bond transaction activities consisting of: offering a bondtransaction, underwriting a bond transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing avalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating bonds, andconsolidating bonds.

The market value data collection circuit 9756 may be structured tomonitor and report on marketplace information relevant to a value of atleast one of a bond issuer, the at least one bond, and an asset. Theasset may be selected from the assets consisting of: a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

The market value data collection circuit 9756 may be further structuredto monitor pricing or financial data for an offset asset item in atleast one public marketplace.

A set of offset asset items 9758 for valuing the asset may beconstructed using a clustering circuit 9762 based on an attribute of theasset. The attribute may be selected from the attributes consisting of acategory, an asset age, an asset condition, an asset history, an assetstorage, and a geolocation.

The smart contract circuit 9722 may be structured to manage a smartcontract 9770 for the at least one transaction. The smart contractcircuit may be further structured to determine a terms and conditions9772 for the at least one bond.

The terms and conditions 9772 may be selected from the group consistingof: a principal amount of debt, a balance of debt, a fixed interestrate, a variable interest rate, a payment amount, a payment schedule, aballoon payment schedule, a specification of assets that back the atleast one bond, a specification of substitutability of assets, a party,an issuer, a purchaser, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, and a consequence of default.

Referring now to FIG. 98, an illustrative and non-limiting examplemethod for monitoring a condition of an issuer for a bond 9800 isdepicted. An example method may include collecting social networkinformation about at least one entity involved in at least onetransaction comprising at least one bond 9802; and classifying acondition of the at least one entity in accordance with a model andbased on the social network information, wherein the model is trainedusing a training data set of a plurality of outcomes related to the atleast one entity 9804; and managing an action related to the at leastone bond in response to the classified condition of the at least oneentity 9806.

An event relevant to at least one of a value, a condition and anownership of at least one asset may be processed 9808. An action relatedto the at least one transaction may be undertaken in response to theevent, wherein managing the action comprises operating the automatedbond management circuit 9810. An automated bond management circuit maybe trained on a training set of a plurality of bond managementactivities to manage an action related to the at least one bond 9812. Anexample method may further include monitoring and reporting onmarketplace information relevant to a value of at least one of a bondissuer, the at least one bond, and an asset 9814.

Referring now to FIG. 99, an illustrative and non-limiting examplesystem for monitoring a condition of an issuer for a bond 9900 isdepicted. The example system may include a controller 9901. Thecontroller 9901 may include a data collection circuit 9912, a marketvalue data collection circuit 9956, an Internet of Things input circuit9944, an Internet of Things data collection circuit 9932, and severalartificial intelligence circuits 9942 including a smart contract circuit9922, an automated bond management circuit 9950, a condition classifyingcircuit 9948, a clustering circuit 9962, and an event processing circuit9952. The condition classifying circuit 9948 may comprise a model 9974trained with a training data set 9946.

The Internet of Things data collection circuit 9932 may be structured tocollect information about at least one entity 9964 involved in at leastone transaction 9930 comprising at least one bond; and a conditionclassifying circuit 9948 may be structured to classify a condition ofthe at least one entity in accordance with a model 9974 and based oninformation from the Internet of Things data collection circuit, whereinthe model is trained using a training data set 9954 of a plurality ofoutcomes related to the at least one entity. The at least one entity maybe selected from the entities consisting of: a bond issuer, a bond, aparty, and an asset. The bond issuer may be selected from the bondissuers consisting of a municipality, a corporation, a contractor, agovernment entity, a non-governmental entity, and a non-profit entity.The bond may be selected from the entities consisting of a municipalbond, a government bond, a treasury bond, an asset-backed bond, and acorporate bond.

The condition classified by the condition classifying circuit 9948 maybe at least one of a default condition, a foreclosure condition, acondition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition or an entity healthcondition.

The Internet of Things data collection circuit 9932 may further includean Internet of Things input circuit 9944 which may be structured toreceive input from a user used to configure a query for information 9910about the at least one entity.

The data collection circuit 9912 may be structured to monitor at leastone of an Internet of Things device, an environmental condition sensor,a crowdsourcing request circuit, a crowdsourcing communication circuit,a crowdsourcing publishing circuit, and an algorithm for queryingnetwork domains for information related to monitored items 9911. Thecondition classifying circuit 9948 may be further structured to classifythe condition in response to the information from the data collectioncircuit 9912.

The data collection circuit 9912 may be further structured to monitor anenvironment selected from the group consisting of: a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle. The condition classifyingcircuit 9948 may be further structured to classify the condition inresponse to the monitored environment.

The at least one bond is backed by at least one asset. The at least oneasset may be selected from the assets consisting of: a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

The event processing circuit 9952 may be structured to process an eventrelevant to at least one of a value, a condition and an ownership of theat least one asset and undertake an action related to the at least onetransaction. The action may be selected from the actions consisting of:a bond transaction, underwriting a bond transaction, setting an interestrate, deferring a payment requirement, modifying an interest rate,validating title, managing inspection, recording a change in title,assessing the value of an asset, calling a loan, closing a transaction,setting tem1s and conditions for a transaction, providing noticesrequired to be provided, foreclosing on a set of assets, modifying termsand conditions, setting a rating for an entity, syndicating bonds, andconsolidating bonds.

The condition classifying circuit 9948 may further include a systemselected from the systems consisting of: a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizingmap, a fuzzy logic system, a random walk system, a random forest system,a probabilistic system, a Bayesian system, and a simulation system.

The automated bond management circuit 9950 may be structured to managean action related to the at least one bond, wherein the automated bondmanagement circuit is trained on a training data set 9954 of a pluralityof bond management activities.

The automated bond management circuit 9950 may be trained on a pluralityof interactions of parties with a plurality of user interfaces involvedin a plurality of bond transaction activities. The plurality of bondtransaction activities may be selected from the bond transactionactivities consisting of: offering a bond transaction, underwriting abond transaction, setting an interest rate, deferring a paymentrequirement, modifying an interest rate, validating title, managinginspection, recording a change in title, assessing a value of an asset,calling a loan, closing a transaction, setting terms and conditions fora transaction, providing notices required to be provided, foreclosing ona set of assets, modifying terms and conditions, setting a rating for anentity, syndicating bonds, and consolidating bonds.

The market value data collection circuit 9956 may be structured tomonitor and report on marketplace information relevant to a value of atleast one of a bond issuer, the at least one bond, and an asset. Theasset may be selected from the assets consisting of: a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

The market value data collection circuit 9956 may be further structuredto monitor pricing or financial data for an offset asset item in atleast one public marketplace.

A set of offset asset items 9958 for valuing the asset may beconstructed using a clustering circuit 9962 based on an attribute of theasset. The attribute may be selected from the attributes consisting of acategory, an asset age, an asset condition, an asset history, an assetstorage, and a geolocation.

The smart contract circuit 9922 may be structured to manage a smartcontract 9970 for the at least one transaction. The smart contractcircuit may be further structured to determine a terms and conditions9772 for the at least one bond.

The terms and conditions may be selected from the group consisting of: aprincipal amount of debt, a balance of debt, a fixed interest rate, avariable interest rate, a payment amount, a payment schedule, a balloonpayment schedule, a specification of assets that back the at least onebond, a specification of substitutability of assets, a party, an issuer,a purchaser, a guarantee, a guarantor, a security, a personal guarantee,a lien, a duration, a covenant, a foreclose condition, a defaultcondition, and a consequence of default.

Referring now to FIG. 100, an illustrative and non-limiting examplemethod for monitoring a condition of an issuer for a bond 10000 isdepicted. An example method may include collecting Internet of Thingsinformation about at least one entity involved in at least onetransaction comprising at least one bond 10002; and classifying acondition of the at least one entity in accordance with a model andbased on the Internet of Things information, wherein the model istrained using a training data set of a plurality of outcomes related tothe at least one entity 10004; and undertaking an action related to theat least one transaction in response to the classified condition of theat least one entity 10006.

An event relevant to at least one of a value, a condition and anownership of at least one asset may be processed 10008. An actionrelated to the at least one transaction may be undertaken in response tothe event 10010. An automated bond management circuit may be trained ona training set of a plurality of bond management activities to manage anaction related to the at least one bond 10012. An example method mayfurther include monitoring and reporting on marketplace informationrelevant to a value of at least one of a bond issuer, the at least onebond, or an asset 10014.

FIG. 101 depicts a system 10100 including an Internet of Things datacollection circuit 10114 structured to collect information about anentity 10102 (e.g., where an entity may be a subsidized loan, a party, asubsidy, a guarantor, a subsidizing party, a collateral, and the like,where a party may be least one of a municipality, a corporation, acontractor, a government entity, a non-governmental entity, and anon-profit entity) involved in a subsidized loan transaction 10104. Inembodiments, the Internet of Things data collection circuit may includea user interface 10116 structured to enable a user to configure a queryfor information about the at least one entity. The system may include acondition classifying circuit 10118 that may include a model 10120structured to classify a parameter 10106 of a subsidized loan 10108(e.g., municipal subsidized loan, a government subsidized loan, astudent loan, an asset-backed subsidized loan, or a corporate subsidizedloan) involved in a subsidized loan transaction, such as based on theinformation from the Internet of Things data collection circuit. Inembodiments, the condition classifying circuit may include a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and the like. The subsidized loan may bebacked by an asset, such as a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, an item of personal property, and the like. The conditionclassified by the condition classifying circuit may be a defaultcondition, a foreclosure condition, a condition indicating violation ofa covenant, a financial risk condition, a behavioral risk condition, acontractual performance condition, a policy risk condition, a financialhealth condition, a physical defect condition, a physical healthcondition, an entity risk condition, an entity health condition, and thelike. The model may be trained using a training data set of a pluralityof outcomes 10110 related to the subsidized loan. For instance, thesubsidized loan may be a student loan and the condition classifyingcircuit may classify a progress of a student toward a degree, aparticipation of a student in a non-profit activity, a participation ofa student in a public interest activity, and the like. The system mayinclude a smart contract circuit 10122 structured to automaticallymodify terms and conditions 10112 of the subsidized loan, such as basedon the classified parameter from the condition classifying circuit. Thesystem may include a configurable data collection and circuit 10124structured to monitor the entity, such as further including a socialnetwork analytic circuit 10130, an environmental condition circuit10132, a crowdsourcing circuit 10134, and an algorithm for querying anetwork domain 10136, where the configurable data collection and circuitmay monitor an environment selected from an environment, such as amunicipal environment, an educational environment, a corporateenvironment, a securities trading environment, a real propertyenvironment, a commercial facility, a warehousing facility, atransportation environment, a manufacturing environment, a storageenvironment, a home, a vehicle, and the like. The system may include anautomated agent 10126 structured to process an event relevant to avalue, a condition and an ownership of the asset and undertake an actionrelated to the subsidized loan transaction to which the asset isrelated, wherein the action may be a subsidized loan transaction,underwriting a subsidized loan transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatinga title, managing an inspection, recording a change in a title,assessing the value of an asset, calling a loan, closing a transaction,setting terms and conditions for a transaction, providing noticesrequired to be provided, foreclosing on a set of assets, modifying termsand conditions, setting a rating for an entity, syndicating a subsidizedloan, consolidating a subsidized loan, and the like. The system mayinclude an automated subsidized loan management circuit 10138 structuredto manage an action related to the at least one subsidized loan, whereinthe automated subsidized loan management circuit is trained on atraining set of subsidized loan management activities. For instance, theautomated subsidized loan management circuit may be trained on aplurality of interactions of parties with a plurality of user interfacesinvolved in a plurality of subsidized loan transaction activities, wherethe plurality of subsidized loan transaction activities may be selectedfrom the activities consisting of offering a subsidized loantransaction, underwriting a subsidized loan transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating a title, managing an inspection, recording a change ina title, assessing a value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating a subsidized loan, and consolidating a subsidized loan. Thesystem may include a blockchain service circuit 10140 structured torecord the modified set of terms and conditions for a subsidized loan,such as in a distributed ledger 10142. The system may include a marketvalue data collection circuit 10128 structured to monitor and report onmarketplace information relevant to a value of an issuer, a subsidizedloan, an asset, and the like, where reporting may be on an assetselected from the assets consisting of a municipal asset, a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property. The market value datacollection circuit may be further structured to monitor pricing orfinancial data for an offset asset item in a public marketplace. A setof offset asset items for valuing the asset may be constructed using aclustering circuit based on an attribute of the asset, where theattribute may be a category, an asset age, an asset condition, an assethistory, an asset storage, a geolocation, and the like. The smartcontract circuit may be structured to manage a smart contract for asubsidized loan transaction, where the smart contract circuit may setterms and conditions for the subsidized loan, where the terms andconditions for the subsidized loan that are specified and managed by thesmart contract circuit may include a principal amount of debt, a balanceof debt, a fixed interest rate, a variable interest rate, a paymentamount, a payment schedule, a balloon payment schedule, a specificationof assets that back the at least one subsidized loan, a specification ofsubstitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, aconsequence of default, and the like.

FIG. 102 depicts a method 10200 including collecting information aboutan entity involved in a subsidized loan transaction 10202. The methodmay include classifying a parameter of a subsidized loan involved in thesubsidized loan transaction based on the information using a modeltrained on a training data set of a plurality of outcomes related to theat least one subsidized loan 10204. The method may include automaticallymodifying terms and conditions of the subsidized loan based on theclassified parameter 10208. The method may include processing an event10210 relevant to a value, a condition and an ownership of an assetrelated to the at least one subsidized loan and undertaking an actionrelated to the subsidized loan transaction to which the asset isrelated. The method may include recording the modified set of terms andconditions for the subsidized loan in a distributed ledger 10212. Themethod may include monitoring and reporting on marketplace information10214 relevant to a value of an issuer, the subsidized loan, the assetrelated to the at least one subsidized loan, and the like.

FIG. 103 depicts a system 10300 including a social network analytic datacollection circuit 10314 structured to collect social networkinformation about an entity 10302 (e.g., where an entity may be asubsidized loan, a party, a subsidy, a guarantor, a subsidizing party, acollateral, and the like, where a party may be least one of amunicipality, a corporation, a contractor, a government entity, anon-governmental entity, and a non-profit entity) involved in asubsidized loan transaction 10304. In embodiments, the social networkanalytic data collection circuit may include a user interface 10316structured to enable a user to configure a query for information aboutthe at least one entity, wherein, in response to the query, the socialnetwork analytic data collection circuit may initiate at least onealgorithm that searches and retrieves data from at least one socialnetwork based on the query. The system may include a conditionclassifying circuit 10318 that may include a model 10320 structured toclassify a parameter 10306 of a subsidized loan 10308 (e.g., municipalsubsidized loan, a government subsidized loan, a student loan, anasset-backed subsidized loan, or a corporate subsidized loan) involvedin a subsidized loan transaction, such as based on the social networkinformation from the social network analytic data collection circuit. Inembodiments, the condition classifying circuit may include a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and the like. The subsidized loan may bebacked by an asset, such as a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, an item of personal property, and the like. The parameterclassified by the condition classifying circuit may be a defaultcondition, a foreclosure condition, a condition indicating violation ofa covenant, a financial risk condition, a behavioral risk condition, acontractual performance condition, a policy risk condition, a financialhealth condition, a physical defect condition, a physical healthcondition, an entity risk condition, an entity health condition, and thelike. The model may be trained using a training data set of a pluralityof outcomes 10310 related to the subsidized loan. For instance, thesubsidized loan may be a student loan and the condition classifyingcircuit may classify a progress of a student toward a degree, aparticipation of a student in a non-profit activity, a participation ofa student in a public interest activity, and the like. The system mayinclude a smart contract circuit 10322 structured to automaticallymodify terms and conditions 10312 of the subsidized loan, such as basedon the classified parameter. The system may include a configurable datacollection and circuit 10324 structured to monitor the entity, such asfurther including a social network analytic circuit 10330, anenvironmental condition circuit 10332, a crowdsourcing circuit 10334,and an algorithm for querying a network domain 10336, where theconfigurable data collection and circuit may monitor an environmentselected from an environment, such as a municipal environment, aneducational environment, a corporate environment, a securities tradingenvironment, a real property environment, a commercial facility, awarehousing facility, a transportation environment, a manufacturingenvironment, a storage environment, a home, a vehicle, and the like. Thesystem may include an automated agent 10326 structured to process anevent relevant to a value, a condition and an ownership of the asset andundertake an action related to the subsidized loan transaction to whichthe asset is related, wherein the action may be a subsidized loantransaction, underwriting a subsidized loan transaction, setting aninterest rate, deferring a payment requirement, modifying an interestrate, validating a title, managing an inspection, recording a change ina title, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating a subsidized loan, consolidating a subsidized loan, and thelike. The system may include an automated subsidized loan managementcircuit 10338 structured to manage an action related to the at least onesubsidized loan, wherein the automated subsidized loan managementcircuit is trained on a training set of subsidized loan managementactivities. For instance, the automated subsidized loan managementcircuit may be trained on a plurality of interactions of parties with aplurality of user interfaces involved in a plurality of subsidized loantransaction activities, where the plurality of subsidized loantransaction activities may be selected from the activities consisting ofoffering a subsidized loan transaction, underwriting a subsidized loantransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating a title, managing an inspection,recording a change in a title, assessing a value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating a subsidized loan, and consolidating a subsidizedloan. The system may include a blockchain service circuit 10340structured to record the modified set of terms and conditions for asubsidized loan, such as in a distributed ledger 10342. The system mayinclude a market value data collection circuit 10328 structured tomonitor and report on marketplace information relevant to a value of anissuer, a subsidized loan, an asset, and the like, where reporting maybe on an asset selected from the assets consisting of a municipal asset,a vehicle, a ship, a plane, a building, a home, real estate property,undeveloped land, a farm, a crop, a municipal facility, a warehouse, aset of inventory, a commodity, a security, a currency, a token of value,a ticket, a cryptocurrency, a consumable item, an edible item, abeverage, a precious metal, an item of jewelry, a gemstone, intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property. The market valuedata collection circuit may be further structured to monitor pricing orfinancial data for an offset asset item in a public marketplace. A setof offset asset items for valuing the asset may be constructed using aclustering circuit based on an attribute of the asset, where theattribute may be a category, an asset age, an asset condition, an assethistory, an asset storage, a geolocation, and the like. The smartcontract circuit may be structured to manage a smart contract for asubsidized loan transaction, where the smart contract circuit may setterms and conditions for the subsidized loan, where the terms andconditions for the subsidized loan that are specified and managed by thesmart contract circuit may include a principal amount of debt, a balanceof debt, a fixed interest rate, a variable interest rate, a paymentamount, a payment schedule, a balloon payment schedule, a specificationof assets that back the at least one subsidized loan, a specification ofsubstitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, aconsequence of default, and the like.

FIG. 104 depicts a method 10400 including collecting social networkinformation about an entity involved in a subsidized loan transaction10402. The method may include classifying a parameter of a subsidizedloan involved in the subsidized loan transaction based on the socialnetwork information using a model trained on a training data set of aplurality of outcomes related to the at least one subsidized loan 10404.The method may include automatically modifying terms and conditions ofthe subsidized loan based on the classified parameter 10408. The methodmay include processing an event 10410 relevant to a value, a conditionand an ownership of an asset and undertaking an action related to thesubsidized loan transaction to which the asset is related. The methodmay include recording the modified set of terms and conditions for thesubsidized loan in a distributed ledger 10412. The method may includemonitoring and reporting on marketplace information 10414 relevant to avalue of an issuer, the subsidized loan, the asset, and the like.

FIG. 105 depicts a system 10500 for automating handling of a subsidizedloan including a crowdsourcing services circuit 10525 structured tocollect information related to a set of entities 10502 involved in a setof subsidized loan transactions 10504. The set of entities may includeentities such as a subsidized loan from a set of subsidized loanscorresponding to the set of subsidized loan transactions, a partyrelated to at least one of the set of subsidized loan transactions, asubsidy corresponding to a subsidized loan from a set of subsidizedloans corresponding to the set of subsidized loan transactions, aguarantor related to at least one of the set of subsidized loantransactions, a subsidy corresponding to a subsidized loan from a set ofsubsidized loans corresponding to the set of subsidized loantransactions, a subsidized party related to at least one of the set ofsubsidized loan transactions, a subsidizing party relate3d to at leastone of the set of subsidized loan transactions, a subsidy correspondingto a subsidized loan from a set of subsidized loans corresponding to theset of subsidized loan transactions, and an item of collateral relatedto at least one of the set of subsidized loan transactions, a subsidycorresponding to a subsidized loan from a set of subsidized loanscorresponding to the set of subsided loan transactions. A set ofsubsidizing parties may include a municipality, a corporation, acontractor, a government entity, a non-governmental entity, and anon-profit entity, and the like. The loan may be a student loan and thecondition classifying circuit classifies at least one of the progress ofa student toward a degree, the participation of a student in anon-profit activity, the participation of the student in a publicinterest activity, and the like. The crowdsourcing services circuit maybe further structured with a user interface 10520 by which a user mayconfigure a query for information about the set of entities and thecrowdsourcing services circuit automatically configures a crowdsourcingrequest based on the query. The set of subsidized loans may be backed bya set of assets 10512, such as a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afa1m, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, an item of personal property, and the like. An example systemmay include a condition classifying circuit 10522 including a model10524 and an artificial intelligence services circuit 10536 structuredto classify a set of parameters 10506 of the set of subsidized loans10510 involved in the transactions based on information fromcrowdsourcing services circuit, where the model may be trained using atraining data set of outcomes 10514 related to subsidized loans. The setof subsidized loans may include at least one of a municipal subsidizedloan, a government subsidized loan, a student loan, an asset-backedsubsidized loan, and a corporate subsidized loan. The conditionclassified by the condition classifying circuit may be a defaultcondition, a foreclosure condition, a condition indicating violation ofa covenant, a financial risk condition, a behavioral risk condition, acontractual performance condition, a policy risk condition, a financialhealth condition, a physical defect condition, a physical healthcondition, an entity risk condition, an entity health condition, and thelike. The artificial intelligence services circuit may a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and the like. An example system may includea smart contract circuit 10526 for automatically modifying the terms andconditions 10518 of a subsidized loan based on the classified set ofparameters from the condition classifying circuit. The smart contractservices circuit may be utilized for managing a smart contract for thesubsidized loan transaction, set terms and conditions for the subsidizedloan, and the like. In embodiments, the set of terms and conditions forthe debt transaction that are specified and managed by the smartcontract services circuit may be selected from among a principal amountof debt, a balance of debt, a fixed interest rate, a variable interestrate, a payment amount, a payment schedule, a balloon payment schedule,a specification of assets that back the subsidized loan, a specificationof substitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default. An example system may include a configurabledata collection and monitoring services circuit 10528 for monitoring theentities such as a set of Internet of Things services, a set ofenvironmental condition sensors, a set of social network analyticservices, a set of algorithms for querying network domains, and thelike. The configurable data collection and monitoring services circuitmay be further structured to monitor an environment such as a municipalenvironment, an educational environment, a corporate environment, asecurities trading environment, a real property environment, acommercial facility, a warehousing facility, a transportationenvironment, a manufacturing environment, a storage environment, a home,a vehicle, and the like. An example system may include an automatedagent circuit 10530 structured to process events relevant to at leastone of the value, the condition, and the ownership of the assets andundertakes an action 10508 related to a subsidized loan transaction towhich the asset is related, such as where the action 10508 may be asubsidized loan transaction, underwriting a subsidized loan transaction,setting an interest rate, deferring a payment requirement, modifying aninterest rate, validating title, managing inspection, recording a changein title, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating subsidized loans, consolidating subsidized loans, and thelike. An example system may include an automated subsidized loanmanagement circuit 10538 structured to manage an action related to thesubsidized loan, where the automated subsidized loan management circuitmay be trained on a training set of subsidized loan managementactivities. The automated subsidized loan management circuit may betrained on a set of interactions of parties with a set of userinterfaces involved in a set of subsidized loan transaction activities,such as offering a subsidized loan transaction, underwriting asubsidized loan transaction, setting an interest rate, deferring apayment requirement, modifying an interest rate, validating title,managing inspection, recording a change in title, assessing the value ofan asset, calling a loan, closing a transaction, setting terms andconditions for a transaction providing notices required to be provided,foreclosing on a set of assets, modifying terms and conditions, settinga rating for an entity, syndicating subsidized loans, consolidatingsubsidized loans, and the like. An example system may include ablockchain services circuit 10540 structured to record the modified setof terms and conditions for the set of subsidized loans in a distributedledger. An example system may include a market value data collectionservice circuit 10532 structured to monitor and report on marketplaceinformation 10534 relevant to the value of at least one of a party, aset of subsidized loans, and a set of assets, where reporting may be ona set of assets such as one of a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property. The market value datacollection service circuit may be further structured to monitor pricingor financial data for items that are similar to the assets in at leastone public marketplace. In embodiments, a set of similar items forvaluing the assets may be constructed using a similarity clusteringalgorithm 10542 based on the attributes of the assets, such as fromamong a category of the assets, asset age, asset condition, assethistory, asset storage, geolocation of assets, and the like.

FIG. 106 depicts a method 10600 for automating handling of a subsidizedloan including collecting information related to a set of entitiesinvolved in a set of subsidized loan transactions 10602, classifying aset of parameters of the set of subsidized loans involved in thetransactions based on an artificial intelligence service, a model, andinformation from a crowdsourcing service, where the model is trainedusing a training data set of outcomes related to subsidized loans 10604;and modifying terms and conditions of a subsidized loan based on theclassified set of parameters 10608. The set of entities may includeentities among a set of subsidized loans, a set of parties, a set ofsubsidies, a set of guarantors, a set of subsidizing parties, and a setof collateral 10610. The set of entities comprise a set of subsidizingparties 10516 and each party of the set of subsidizing parties mayinclude a municipality, a corporation, a contractor, a governmententity, a non-governmental entity, and a non-profit entity 10612. Theset of subsidized loans may include a municipal subsidized loan, agovernment subsidized loan, a student loan, an asset-backed subsidizedloan, and a corporate subsidized loan 10614. The subsidized loan may bea student loan where the condition classifying system classifies atleast one of the progress of a student toward a degree, theparticipation of a student in a non-profit activity, or a participationof the student in a public interest activity 10618.

FIG. 107 depicts a system 10700 including an asset identificationservice circuit 10712 structured to interpret assets 10724 correspondingto a financial entity 10722 configured to take custody of the assets(e.g., identifying assets for which a bank may take custody), where anidentity management service circuit 10714 may be structured toauthenticate identifiers 10728 (e.g., including a credential 10730)corresponding to actionable entities 10726 (e.g., an owner, abeneficiary, an agent, a trustee, a custodian, and the like) entitled totake action with respect to the assets. For example, a group offinancial entities may have permissions with respect to actions to betaken with respect to an asset. A blockchain service circuit 10716 maybe structured to store a plurality of asset control features 10732 in ablockchain structure 10718, where the blockchain structure may include adistributed ledger configuration 10720. For instance, transactionalevents may be stored in a distributed ledger in the blockchain structurewhere the financial entity and actionable entities may have distributedaccess through the blockchain structure to share and distribute theasset events. A financial management circuit 10710 may be structured tocommunicate the interpreted assets and authenticated identifiers to theblockchain service circuit for storage in the blockchain structure asasset control features, wherein the asset control features are recordedin the distributed ledger configuration as asset events 10734 (e.g., atransfer of title, death of an owner, disability of an owner, bankruptcyof an owner, foreclosure, placement of a lien, use of assets ascollateral, designation of a beneficiary, undertaking a loan againstassets, providing a notice with respect to assets, inspection of assets,assessment of assets, reporting on assets for taxation purposes,allocation of ownership of assets, disposal of assets, sale of assets,purchase of assets, a designation of an ownership status, and the like).A data collection circuit 10702 may be structured to monitor theinterpretation of the plurality of assets, authentication of theplurality of identifiers, and the recording of asset events, where tdata collection circuit may be communicatively coupled with an Internetof Things system, a camera system, a networked monitoring system, aninternet monitoring system, a mobile device system, a wearable devicesystem, a user interface system, and an interactive crowdsourcingsystem. A smart contract circuit 10704 may be structured to manage thecustody of the assets, where an asset event related to the plurality ofassets may be managed by the smart contract circuit based on terms andconditions 10708 embodied in a smart contract configuration 10706 andbased on data collected by the data collection service circuit. Inembodiments, the asset identification service circuit, identitymanagement service circuit, blockchain service circuit, and thefinancial management circuit may include a corresponding applicationprogramming interface (API) component structured to facilitatecommunication among the circuits of the system, such as where thecorresponding API components of the circuits further include userinterfaces structured to interact with users of the system.

FIG. 108 depicts a method 10800 including interpreting assetscorresponding to a financial entity configured to take custody of theplurality of assets 10802, such as where the interpreting of the assetsmay include identifying the plurality of assets for which a financialentity is responsible for taking custody. The method may includeauthenticating identifiers (e.g., including a credential) correspondingto actionable entities (e.g., owner, a beneficiary, an agent, a trustee,and a custodian) entitled to take action with respect to the pluralityof assets 10804, such as where authenticating the identifiers includesverifying the identifiers corresponding to actionable entities areentitled to take action with respect to the assets. The method mayinclude storing a plurality of asset control features in a blockchainstructure 10808 (e.g., including a distributed ledger configuration) Theblockchain structure may be provided in conjunction with a block-chainmarketplace, utilize an automated blockchain-based transactionapplication, the blockchain structure may be a distributed blockchainstructure across a plurality of asset nodes, and the like. The methodmay include communicating the interpreted assets and authenticatedidentifiers for storage in the blockchain structure as asset controlfeatures, where the asset control features may be recorded in thedistributed ledger configuration as asset events 10810. The method mayinclude monitoring the interpretation of the assets, authentication ofthe identifiers, and the recording of asset events 10812, such as whereasset events may include transfer of title, death of an owner,disability of an owner, bankruptcy of an owner, foreclosure, placementof a lien, use of assets as collateral, designation of a beneficiary,undertaking a loan against assets, providing a notice with respect toassets, inspection of assets, assessment of assets, reporting on assetsfor taxation purposes, allocation of ownership of assets, disposal ofassets, sale of assets, purchase of assets, and designation of anownership status. In embodiments, monitoring may be executed by anInternet of Things system, a camera system, a networked monitoringsystem, an internet monitoring system, a mobile device system, awearable device system, a user interface system, an interactivecrowdsourcing system, and the like. The method may include managing thecustody of the assets, where an asset event related to the plurality ofassets may be based on terms and conditions embodied in a smart contractconfiguration and based on data collected by a data collection servicecircuit 10814. The method may include sharing and distributing the assetevents with the plurality of actionable entities 10818. The method mayinclude storing asset transaction data in the blockchain structure basedon interactions between actionable entities 10820. An asset may includea virtual asset tag where interpreting the assets comprises identifyingthe virtual asset tag (e.g., storing of the asset control features mayinclude storing virtual asset tag data, such as where the virtual assettag data is location data, tracking data, and the like. For instance, anidentifier corresponding to the financial entity or actionable entitiesmay be stored as virtual asset tag data.

FIG. 109 depicts a system 10900 including a lending agreement storagecircuit 10902 structured to store a lending agreement data 10904including a lending agreement 10914, wherein the lending agreement mayinclude a lending condition data 10916. In embodiments, the lendingcondition data may include a terms and condition data 10918 of the atleast one lending agreement related to a foreclosure condition 10922 onan asset 10920 that provides a collateral condition 10924 related to acollateral asset 10926, such as for securing a repayment obligation10928 of the lending agreement. The system may include a data collectionservices circuit 10906 structured to monitor the lending condition dataand to detect a default condition 10908 based on a change to the lendingcondition data. Further, the data collection services circuit mayinclude an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, and aninteractive crowdsourcing system. The system may include a smartcontract services circuit 10910 structured to, when the defaultcondition is detected by the data collection services circuit, interpretthe default condition 10912 and communicate a default conditionindication 10930, such as to initiate a foreclosure procedure 10932based on the collateral condition. For instance, the foreclosureprocedure may configure and initiate a listing of the collateral asseton a public auction site, configure and deliver a set of transportinstructions for the collateral asset, configure a set of instructionsfor a drone to transport the collateral asset, configure a set ofinstructions for a robotic device to transport the collateral asset,initiate a process for automatically substituting a set of substitutecollateral, initiate a collateral tracking procedure, initiates acollateral valuation process, initiates a message to a borrowerinitiating a negotiation regarding the foreclosure, and the like. Thedefault condition indication may be communicated to a smart lock and asmart container to lock the collateral asset. The negotiation may bemanaged by a robotic process automation system trained on a training setof foreclosure negotiations, and may relate to modification of interestrate, payment terms, collateral for the lending agreement, and the like.In embodiments, each of the lending agreement storage circuit, datacollection services circuit, and smart contract services circuit mayfurther include a corresponding application programming interface (API)component structured to facilitate communication among the circuits ofthe system, where the corresponding API components of the circuits mayinclude user interfaces structured to interact with a plurality of usersof the system.

FIG. 110 depicts a method 11000 for facilitating foreclosure oncollateral, the method including storing a lending agreement dataincluding a lending agreement, where the lending agreement may include alending condition data, such as where the lending condition dataincludes a terms and condition data of the lending agreement related toa foreclosure condition on an asset that provides a collateral conditionrelated to a collateral asset for securing a repayment obligation of theat least one lending agreement 11002. The method may include monitoringthe lending condition data and to detect a default condition based on achange to the lending condition data 11004. The method may includeinterpreting the default condition 11008 and communicating a defaultcondition indication that initiates a foreclosure procedure based on thecollateral condition 11010. For instance, the foreclosure procedure mayconfigure and initiate a listing of the collateral asset on a publicauction site, configure and deliver a set of transport instructions forthe collateral asset, configure a set of instructions for a drone totransport the collateral asset, configure a set of instructions for arobotic device to transport the collateral asset, initiate a process forautomatically substituting a set of substitute collateral, initiate acollateral tracking procedure, initiates a collateral valuation process,initiates a message to a borrower initiating a negotiation regarding theforeclosure, and the like 11014. The default condition indication may becommunicated to a smart lock and a smart container to lock thecollateral asset 11012. The negotiation may be managed by a roboticprocess automation system trained on a training set of foreclosurenegotiations 11018, and may relate to modification of interest rate,payment terms, collateral for the lending agreement, and the like. Inembodiments, communications may be provided by a correspondingapplication programming interface (API) 11020, where the correspondingAPI may include user interfaces structured to interact with a pluralityof users.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example platform or system mayinclude a blockchain service circuit structured to interpret a pluralityof access control features corresponding to a plurality of partiesassociated with a loan; a data collection circuit structured tointerpret entity information corresponding to a plurality of entitiesrelated to a lending transaction corresponding to the loan; a smartcontract circuit structured to specify loan terms and conditionsrelating to the loan; and a loan management circuit structured to:interpret loan related events in response to the entity information, theplurality of access control features, and the loan terms and conditions,wherein the loan related events are associated with the loan; implementloan related activities in response to the entity information, theplurality of access control features, and the loan terms and conditions,wherein the loan related activities are associated with the loan; andwherein each of the blockchain service circuit, the data collectioncircuit, the smart contract circuit, and the loan management circuitfurther comprise a corresponding application programming interface (API)component structured to facilitate communication among the circuits ofthe system.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the plurality of entities eachcomprise at least one entity selected from the entities consisting of: alender, a borrower, a guarantor, equipment related to the loan, goodsrelated to the loan, a system related to the loan, a fixture related tothe loan, a building, a storage facility, and an item of collateral.

An example system may include at least one of the plurality of entitiescomprises an item of collateral, and wherein the data collection circuitis further structured to interpret a condition of the item ofcollateral, wherein the item of collateral comprises at least one itemselected from the items consisting of: a vehicle, a ship, a plane, abuilding, a home, a real estate property, an undeveloped land property,a farm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, a tool, an item of machinery,and an item of personal property.

An example system may include wherein the data collection circuitfurther comprises at least one system selected from the systemsconsisting of: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

An example system may include wherein the loan related events eachcomprise at least one event selected from the events consisting of: aloan request, a loan offer, a loan acceptance, a provision ofunderwriting information for a loan, a provision of a credit report, adeferral of a payment, a requested deferral of a payment, anidentification of collateral, a validation of title for collateral, avalidation of title for a security, an inspection of property, a changein condition for at least one of the plurality of entities, a change invalue of an entity, a change in value for collateral, a change in valuefor a security, a change in a job status of at least one of the parties,a change in a financial rating of a lender, a provision of insurance forthe loan, a provision of evidence of insurance for a property, aprovision of eligibility for a loan, an identification of security forthe loan, an execution of underwriting the loan, a payment of the loan,a default of the loan, a calling of the loan, a closing of the loan, achange in the specified loan terms and conditions, an initialspecification of the loan terms and conditions, and a foreclosure of aproperty subject to the loan.

An example system may include wherein the loan terms and conditions eachcomprise at least one member selected from the group consisting of: aprincipal amount of the loan, a balance of the loan, a fixed interestrate, a variable interest rate description, a payment amount, a paymentschedule, a balloon payment schedule, a collateral specification, acollateral substitution description, a description of at least one ofthe parties, a guarantee description, a guarantor description, asecurity description, a personal guarantee, a lien, a foreclosurecondition, a default condition, a consequence of default, a covenantrelated to any one of the foregoing, and a duration of any one of theforegoing.

An example system may include wherein at least one of the partiescomprises at least one party selected from the parties consisting of: aprimary lender, a secondary lender, a lending syndicate, a corporatelender, a government lender, a bank lender, a secured lender, bondissuer, a bond purchaser, an unsecured lender, a guarantor, a providerof security, a borrower, a debtor, an underwriter, an inspector, anassessor, an auditor, a valuation professional, a government official, agovernment agency, and an accountant.

An example system may include wherein loan related activities eachcomprise at least one activity selected from the activities consistingof: finding at least one of the parties interested in participating in aloan transaction, an application for the loan, underwriting the loan,forming a legal contract for the loan, monitoring performance of theloan, making payments on the loan, restructuring or amending the loan,settling the loan, monitoring collateral for the loan, forming asyndicate for the loan, foreclosing on the loan, and closing a loantransaction and wherein the loan comprises at least one loan typeselected from the loan types consisting of: an auto loan, an inventoryloan, a capital equipment loan, a bond for performance, a capitalimprovement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein the smart contract circuit isfurther structured to perform a contract related loan action in responseto the entity information.

An example system may include wherein the contract related loan actioncomprises at least one action selected from the actions consisting of:offering the loan, accepting the loan, underwriting the loan, setting aninterest rate for the loan, deferring a payment requirement for theloan, modifying an interest rate for the loan, validating title forcollateral of the loan, recording a change in title, assessing the valueof collateral, initiating inspection of collateral, calling the loan,closing the loan, modifying the terms and conditions for the loan,providing a notice to one of the parties, providing a required notice toa borrower of the loan, and foreclosing on a property subject to theloan.

An example system may further include an automated agent circuitstructured to interpret an event relevant to the loan, and to perform anaction related to the loan in response to the event relevant to theloan, wherein the event relevant to the loan comprises an event relevantto at least one of: the value of the loan, a condition of collateral ofthe loan, or an ownership of collateral of the loan and wherein theaction related to the loan comprises at least one of: modifying theterms and conditions for the loan, providing a notice to one of theparties, providing a required notice to a borrower of the loan, andforeclosing on a property subject to the loan.

An example system may include wherein the corresponding API componentsof the circuits further comprise user interfaces structured to interactwith a plurality of users of the system.

An example system may include wherein the plurality of users eachcomprise one of the plurality of parties or one of the plurality ofentities and wherein at least one of the plurality of users comprisesone of a prospective party or a prospective entity.

An example system may include wherein each of the user interfaces isconfigured to be responsive to the plurality of access control features.

In embodiments, provided herein is a method for providing access controlfor loan terms and conditions on a distributed ledger. An example methodmay include interpreting a plurality of access control featurescorresponding to a plurality of parties associated with a loan from adistributed ledger; interpreting entity information corresponding to aplurality of entities related to a lending transaction corresponding tothe loan; specifying loan terms and conditions relating to the loan;interpreting loan related events in response to the entity information,the plurality of access control features, and the loan terms andconditions, wherein the loan related events are associated with theloan.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample method can include wherein at least one of the plurality ofentities comprises an item of collateral, the method further comprisinginterpreting a condition of the item of collateral.

An example method can further include performing a contract related loanaction in response to the entity information.

An example method can include wherein performing the contract relatedloan action comprises at least one action selected from the actionsconsisting of: offering the loan, accepting the loan, underwriting theloan, setting an interest rate for the loan, deferring a paymentrequirement for the loan, modifying an interest rate for the loan,validating title for collateral of the loan, recording a change intitle, assessing the value of collateral, initiating inspection ofcollateral, calling the loan, closing the loan, modifying the terms andconditions for the loan, providing a notice to one of the parties,providing a required notice to a borrower of the loan, and foreclosingon a property subject to the loan.

An example method can further include interpreting an event relevant tothe loan, and performing an action related to the loan in response tothe event relevant to the loan, wherein the event relevant to the loancomprises an event relevant to at least one of: the value of the loan, acondition of collateral of the loan, or an ownership of collateral ofthe loan and wherein performing the action related to the loan comprisesat least one of: modifying the terms and conditions for the loan,providing a notice to one of the parties, providing a required notice toa borrower of the loan, and foreclosing on a property subject to theloan.

An example method can further include providing a user interface to auser, wherein the user comprises at least one of: one of the pluralityof parties, one of the plurality of entities, a prospective party, or aprospective entity, wherein the providing the user interface is furtherresponsive to the plurality of access control features.

An example method can further include creating a smart lending contractfor the loan and recording the smart lending contract as blockchaindata.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example platform or system mayinclude a blockchain service circuit structured to interpret a pluralityof access control features corresponding to a plurality of partiesassociated with a secured loan, and a data collection circuit structuredto receive first collateral data from at least one sensor associatedwith an item of collateral used to secure the loan, receive secondcollateral data regarding an environment of the item of collateral froman Internet of Things circuit, and associate the collateral data with aunique identifier associated with the item of collateral, wherein theblockchain service circuit is further structured to store the uniqueidentifier and associated collateral data as blockchain data. Theexample platform or system may further include a smart contract circuitstructured to create a smart lending contract, and a secure accesscontrol circuit structured to receive access control instructions from alender of the secured loan via an access control interface, wherein thesecure access control circuit is further structured to provideinstructions to the blockchain service circuit regarding access to theblockchain data associated with the item of collateral, wherein each ofthe blockchain service circuit, the data collection circuit, the secureaccess control circuit, and the Internet of Things circuit furthercomprise a corresponding application programming interface (API)component structured to facilitate communication among the circuits ofthe system.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the sensor associated with the itemof collateral is positioned on in a location selected from the listconsisting of on the item of collateral, on a container for the item ofcollateral, and on a package of the item of collateral.

An example system may include wherein the data collection circuit isfurther structured to interpret a condition of the item of collateral inresponse to a subset of the received collateral data.

An example system may include wherein the item of collateral is selectedfrom among the list of items consisting of: a vehicle, a ship, a plane,a building, a home, a real estate property, an undeveloped landproperty, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, an item ofintellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, a tool, an item ofmachinery, and an item of personal property.

An example system may include wherein the secured loan is at least oneof an auto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

An example system may include wherein the environment of the item ofcollateral is selected from, the list of environments consisting of areal property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle.

An example system may include wherein the at least one sensor isselected from the group consisting of an image capture device, athermometer, a pressure gauge, a humidity sensor, a velocity sensor, anacceleration sensor, a rotational sensor, a torque sensor, a scale,chemical, magnetic field, electrical field, and position sensors.

An example system may further include a reporting circuit structured toreport a collateral event related to an aspect of the collateralselected from the list of aspects consisting of: a value of the item ofcollateral, a condition of the item of collateral, and an ownership ofthe item of collateral.

An example system may further include an automated agent circuitstructured to interpret the collateral event and to perform aloan-related action in response to the collateral event.

An example system may include wherein the loan-related action isselected from among the actions consisting of: offering a loan,accepting a loan, underwriting a loan, setting an interest rate for aloan, deferring a payment requirement, modifying an interest rate for aloan, validating title for collateral, recording a change in title,assessing the value of collateral, initiating inspection of collateral,calling a loan, closing a loan, setting terms and conditions for a loan,providing notices required to be provided to a borrower, foreclosing onproperty subject to a loan, and modifying terms and conditions for aloan.

An example system may further include a collateral classificationcircuit structured to identify a group of offset items of collateral,wherein each member of the group of offset items of collateral and theitem of collateral share a common attribute.

An example system may include wherein the common attribute is selectedfrom a list of attributes consisting of: a category of the item ofcollateral, an age of the item of collateral, a condition of the item ofcollateral, a history of the item of collateral, an ownership of theitem of collateral, a caretaker of the item of collateral, a security ofthe item of collateral, a condition of an owner of the item ofcollateral, a lien on the item of collateral, a storage condition of theitem of collateral, a geolocation of the item of collateral, and ajurisdictional location of the item of collateral.

An example system may further include a market value data collectioncircuit structured to monitor and report on marketplace informationrelevant to a value of the item of collateral or at least one of thegroup of offset items of collateral.

An example system may include wherein the market value data collectioncircuit is further structured to monitor a price or financial data theitem of collateral or at least one of the group of offset items ofcollateral in at least one public marketplace.

An example system may include wherein the market value data collectioncircuit is further structured to report the monitored one of the priceor the financial data.

An example system may include wherein the smart contract circuit isfurther structured to modify a term or condition of the loan based onthe marketplace information for offset items of collateral relevant tothe value of the item of collateral.

An example system may further include a smart contract services circuitstructured to manage a smart contract for the secured loan.

An example system may include wherein the smart contract servicescircuit is further structured to set terms and conditions related to theitem of collateral securing the loan.

An example system may include wherein the terms and conditions areselected from a list consisting of: a specification of the item ofcollateral, a specification of substitutability of the item ofcollateral, a specification of condition of the item of collateral, aspecification related to liens on the item of collateral, aspecification related to the security of the item of collateral, and aspecification related to the environment of the item of collateral.

In embodiments, provided herein is a method for automated smart contractcreation and collateral assignment. An example method may includereceiving first collateral data from a sensor associated with an item ofcollateral used to secure a loan, receiving second collateral dataregarding an environment of the item of collateral, associating thecollateral data with a unique identifier associated with the item ofcollateral, creating a smart lending contract, storing the uniqueidentifier and the collateral data in a blockchain structure, receivingaccess control instructions from a lender of the secured loan,interpreting a plurality of access control features, and providingaccess to the data regarding the item of collateral.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may further include interpreting a condition of the itemof collateral in response to a subset of the received collateral data.

An example method may further include identifying a collateral eventfrom the condition of the item of collateral and reporting thecollateral event, wherein the collateral event is relevant to acollateral characteristic selected from the list consisting of: a valueof the item of collateral, a condition of the item of collateral, and anownership of the item of collateral.

An example method may further include determining a value for the itemof collateral.

An example method may further include interpreting the collateral event;and performing a loan-related action in response to the collateralevent.

An example method may further include identifying a group of offsetcollateral, wherein each member of the group of offset items ofcollateral and the item of collateral share a common attribute.

An example method may further include monitoring a marketplace forinformation relevant to a value of the item of collateral or at leastone of the group of offset items of collateral and modifying a term ofcondition of the loan based on the marketplace information.

An example method may further include creating a smart lending contractfor the loan.

An example method may further include receiving access controlinstructions, interpreting a plurality of access control features, andproviding access to the collateral data.

In embodiments, provided herein is a system for handling a loan. Anexample platform, system, or apparatus may include a blockchain servicecircuit structured to interface with a distributed ledger; a datacollection circuit structured to receive data related to a plurality ofitems of collateral or data related to environments of the plurality ofitems of collateral; a valuation circuit structured to determine a valuefor each of the plurality of items of collateral based on a valuationmodel and the received data; a smart contract circuit structured tointerpret a smart lending contract for a loan, and to modify the smartlending contract by assigning, based on the determined value for each ofthe plurality of items of collateral, at least a portion of theplurality of items of collateral as security for the loan such that thedetermined value of the of the plurality of items of collateral issufficient to provide security for the loan. The blockchain servicecircuit may be further structured to record the assigned at least aportion of items of collateral to an entry in the distributed ledger,wherein the entry is used to record events relevant to the loan. Each ofthe blockchain service circuit, the data collection circuit, thevaluation circuit and the smart contract circuit may further include acorresponding application programming interface (API) componentstructured to facilitate communication among the circuits of the system.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein modifying the smart lending contractfurther comprises specifying terms and conditions that govern an itemselected from the list consisting of a loan term, a loan condition, aloan-related event, and a loan-related activity.

An example system may include wherein the terms and conditions eachcomprise at least one member selected from the group consisting of: aprincipal amount of the loan, a balance of the loan, a fixed interestrate, a variable interest rate description, a payment amount, a paymentschedule, a balloon payment schedule, a collateral specification, acollateral substitution description, a description of at least one ofthe parties, a guarantee description, a guarantor description, asecurity description, a personal guarantee, a lien, a foreclosurecondition, a default condition, a consequence of default, a covenantrelated to any one of the foregoing, and a duration of any one of theforegoing.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein the item of collateral comprisesat least one item selected from the items consisting of: a vehicle, aship, a plane, a building, a home, a real estate property, anundeveloped land property, a farm, a crop, a municipal facility, awarehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone, anitem of intellectual property, an intellectual property right, acontractual right, an antique, a fixture, an item of furniture, a tool,an item of machinery, and an item of personal property.

An example system may include wherein the data collection circuit isfurther structured to receive outcome data related to the loan and acorresponding item of collateral, and wherein the valuation circuitcomprises an artificial intelligent circuit structured to iterativelyimprove the valuation model based on the outcome data.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report marketplace information relevant to the value of at least oneof the plurality of items of collateral.

An example system may include wherein the market value monitoringcircuit is further structured to monitor pricing or financial data foritems that are similar to the item of collateral in at least one publicmarketplace.

An example system may further include a clustering circuit structured toidentify a set of similar items for use in valuing the item ofcollateral based on similarity to an attribute of the collateral.

An example system may include wherein the attribute of the collateral isselected from among a list of attributes consisting of: a category ofthe collateral, an age of the collateral, a condition of the collateral,a history of the collateral, a storage condition of the collateral, anda geolocation of the collateral.

An example system may include wherein the data collection circuit isfurther structured to interpret a condition of the item of collateral.

An example system may include wherein the data collection circuitfurther comprises at least one system selected from the systemsconsisting of: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may further include a loan management circuitstructured to interpret an event relevant to the loan, and to perform anaction related to the loan in response to the event relevant to theloan.

An example system may include wherein the event relevant to the loancomprises an event relevant to at least one of: a value of the loan, acondition of collateral of the loan, or an ownership of collateral ofthe loan.

An example system may include wherein the action related to the loancomprises at least one of: modifying the terms and conditions for theloan, providing a notice to one of the parties, providing a requirednotice to a borrower of the loan, and foreclosing on a property subjectto the loan.

An example system may include wherein the corresponding API componentsof the circuits further comprise user interfaces structured to interactwith a plurality of users of the system.

An example system may include wherein the plurality of users eachcomprise: one of the plurality of parties, one of the plurality ofentities, or a representative of any one of the foregoing.

An example system may include wherein at least one of the plurality ofusers comprises: a prospective party, a prospective entity, or arepresentative of any one of the foregoing.

In embodiments, provided herein is a method for handling a loan. Anexample method may include receiving data related to a plurality ofitems of collateral; setting a value for each of the plurality of itemsof collateral; assigning at least a portion of the plurality of items ofcollateral as security for a loan; and recording the assigned at least aportion of the plurality of items of collateral to an entry in adistributed ledger, wherein the entry is used to record events relevantto the loan.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may further include modifying a smart lending contractfor the loan.

An example method may further include modifying a smart lending contractcomprises adjusting or specifying terms and conditions for the loan.

An example method may include wherein the terms and conditions are eachselected from the list consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, a party,a guarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default.

An example method may further include receiving outcome data related tothe loan; and iteratively improving a valuation model based on theoutcome data and corresponding collateral.

An example method may further include

monitoring marketplace information relevant to the value of at least oneof the plurality of items of collateral.

An example method may further include identifying a set of items similarto one of the plurality of items of collateral based on similarity to anattribute of the one of the plurality of items of collateral.

An example method may further include interpreting a condition of theone of the plurality of items of collateral.

An example method may further include reporting events related to avalue of the one of the plurality of items of collateral, a condition ofthe one of the plurality of items of collateral, or an ownership of theone of the items of collateral.

An example method may further include interpreting an event relevant to:a value of one of the plurality of items of collateral, a condition ofone of the plurality of items of collateral, or an ownership of one ofthe plurality of items of collateral; and performing an action relatedto the secured loan in response to the event relevant to the one of theplurality of items of collateral for said secured loan.

An example method may further include wherein the loan-related action isselected from among the actions consisting of: offering a loan,accepting a loan, underwriting a loan, setting an interest rate for aloan, deferring a payment requirement, modifying an interest rate for aloan, validating title for collateral, recording a change in title,assessing the value of collateral, initiating inspection of collateral,calling a loan, closing a loan, setting terms and conditions for a loan,providing notices required to be provided to a borrower, foreclosing onproperty subject to a loan, and modifying terms and conditions for aloan.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example platform or system mayinclude a blockchain service circuit structured to interface with adistributed ledger; a data collection circuit structured to receive datarelated to a set of items of collateral that provide security for aloan: a smart contract circuit structured to create a smart lendingcontract for the loan and assign at least a portion of the set of itemsof collateral to the loan, thereby creating an assigned set of items ofcollateral; wherein the blockchain service circuit is further structuredto record the assigned set of items of collateral to a loan-entry in thedistributed ledger, and wherein each of the blockchain service circuit,the data collection circuit, and the smart contract circuit furthercomprise a corresponding application programming interface (API)component structured to facilitate communication among the circuits ofthe system.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the data collection circuit isfurther structured to receive data related to an environment of theassigned set of items of collateral.

An example system may include wherein the smart contract circuit isfurther structured to specify a term or condition of the loan thatgoverns an item selected from the list consisting of: a loan term, aloan condition, a loan-related event, and a loan-related activity,wherein the terms and conditions of the loan each comprise at least onemember selected from the group consisting of: a principal amount of theloan, a balance of the loan, a fixed interest rate, a variable interestrate description, a payment amount, a payment schedule, a balloonpayment schedule, a collateral specification, a collateral substitutiondescription, a description of at least one party to the loan, aguarantee description, a guarantor description, a security description,a personal guarantee, a lien, a foreclosure condition, a defaultcondition, a consequence of default, a covenant related to any one ofthe foregoing, and a duration of any one of the foregoing.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein the assigned set of items ofcollateral comprises at least one item selected from the itemsconsisting of: a vehicle, a ship, a plane, a building, a home, a realestate property, an undeveloped land property, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, a tool, an item of machinery, and an item ofpersonal property.

An example system may further include a valuation circuit structured todetermine a value for each of the set of items of collateral or theassigned set of items of collateral, based on a valuation model and thereceived data, wherein the valuation circuit comprises a valuation modelimprovement circuit, wherein the valuation model improvement circuitmodifies the valuation model based on a first set of valuationdeterminations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security.

An example system may further include wherein the valuation modelimprovement circuit comprises at least one system from the list ofsystems consisting of: a machine learning system, a model-based system,a rule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, a hybrid system, and a hybrid systemincluding at least two of any of the foregoing.

An example system may further include a collateral classificationcircuit structured to identify a group of offset items of collateral,wherein each member of the group of offset items of collateral and atleast one of the assigned set of items of collateral share a commonattribute, wherein the common attribute is selected from a list ofattributes consisting of: a category of the items, an age of the items,a condition of the items, a history of the items, an ownership of theitems, a caretaker of the items, a security of the items, a condition ofan owner of the items, a lien on the items, a storage condition of theitems, a geolocation of the items, and a jurisdictional location of theitems.

An example system may further include, wherein the valuation circuitfurther includes a market value data collection circuit structured tomonitor and report marketplace information for offset items ofcollateral relevant to the value of at least one of the assigned set ofitems of collateral. An example system may further include wherein thesmart contract circuit is further structured to apportion, among a setof lenders, the value for one of the assigned set of items ofcollateral.

An example system may include wherein the loan-entry in the distributedledger further comprises priority information related to a lender, andwherein an apportionment of value is based on the priority informationfor the lender, wherein the lender is selected from a list consistingof: a primary lender, a secondary lender, a lending syndicate, acorporate lender, a government lender, a bank lender, a secured lender,a bond issuer, and an unsecured lender.

An example system may further include, wherein the data collectioncircuit comprises at least one system selected from systems consistingof: an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

An example system may further include wherein the data collectioncircuit is further structured to identify a collateral event based onthe received data, wherein the collateral event is related to a value ofone of the assigned set of items of collateral, a condition of one ofthe assigned set of items of collateral, or an ownership of one of theassigned set of items of collateral and further including an automatedagent circuit structured to perform a collateral-related action inresponse to the collateral event, wherein the collateral-related actionis selected from among the actions consisting of: validating title forthe one of the assigned set of items of collateral, recording a changein title for the one of the assigned set of items of collateral,assessing the value of the one of the assigned set of items ofcollateral, initiating inspection of the one of the assigned set ofitems of collateral, initiating maintenance of the one of the assignedset of items of collateral, initiating security for the one of theassigned set of items of collateral, and modifying terms and conditionsfor the one of the assigned set of items of collateral.

An example system may include wherein the automated agent circuit isfurther structured to perform a loan-related action in response to thecollateral event, wherein the loan-related action is selected from thelist of actions consisting of: offering the loan, accepting the loan,underwriting the loan, setting an interest rate for a loan, deferring apayment requirement, modifying the interest rate for the loan, callingthe loan, closing the loan, setting terms and conditions for the loan,providing notices required to be provided to a borrower, foreclosing onproperty subject to the loan, and modifying terms and conditions for theloan.

In embodiments, provided herein is a method for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example method may includereceiving data related to a set of items of collateral that providesecurity for a loan; creating a smart lending contract for the loan;recording the set of items of collateral in the smart lending contract;and recording a loan-entry in a distributed ledger, wherein theloan-entry comprises one of the smart lending contract or a reference tothe smart lending contract.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample method may further include receiving data related to anenvironment of one of the set of items of collateral.

An example method may further include determining a value for each ofthe set of items of collateral based on a valuation model and thereceived data and modifying the valuation model based on a first set ofvaluation determinations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security.

An example method may further include apportioning, among a set oflenders, the value of one of the set of items of collateral.

An example method may further include determining a collateral eventbased on at least one of the value of one of the set of items ofcollateral and the received data and performing a loan-related action inresponse to the collateral event, wherein the loan-related action isselected from the list of actions consisting of: offering the loan,accepting the loan, underwriting the loan, setting an interest rate fora loan, deferring a payment requirement, modifying the interest rate forthe loan, calling the loan, closing the loan, setting terms andconditions for the loan, providing notices required to be provided to aborrower, foreclosing on property subject to the loan, and modifyingterms and conditions for the loan.

An example method may further include performing a collateral-relatedaction in response to the collateral event, wherein thecollateral-related action is selected from the list of actionsconsisting of: validating title for the one of the set of items ofcollateral, recording a change in title for the one of the set of itemsof collateral, assessing the value of the one of the set of items ofcollateral, initiating inspection of the one of the set of items ofcollateral, initiating maintenance of the one of the set of items ofcollateral, initiating security for the one of the set of items ofcollateral, and modifying terms and conditions for the one of the set ofitems of collateral.

An example method may further include identifying a group of offsetitems of collateral, wherein the group of offset items of collateral andat least one of the set of items of collateral share a common attribute;monitoring marketplace information for data related to the group ofoffset items of collateral; updating the value of the at least one ofthe set of items based on the monitored data; and updating theloan-entry in the distributed ledger with the updated value.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example platform or system mayinclude a data collection circuit structured to receive data related toan item of collateral that provides security for a loan; a valuationcircuit structured to determine a value for the item of collateral basedon the received data and a valuation model; a smart contract circuitstructured to create a smart lending contract, wherein the smart lendingcontract specifies a covenant defining a required value of the item ofcollateral; and a loan management circuit including: a value comparisoncircuit structured to compare the value of the item and the specifiedcovenant and determine a collateral satisfaction value; an automatedagent circuit structured to automatically implement loan relatedactivities in response to the collateral satisfaction value, wherein theloan related activities comprise: issuing a notice of default or aforeclosure action.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the smart contract circuit is furtherstructured to: determine at least one of a term or a condition for thesmart lending contract in response to the collateral satisfaction value;and modify the smart lending contract to include the at least one of theterm or the condition, wherein the at least one of the term or thecondition is related to a loan component selected from the loancomponents consisting of: a loan party, a loan collateral, aloan-related event, and a loan-related activity.

An example system may include wherein the at least one of a term orcondition is selected from the list consisting of: a principal amount ofthe loan, a balance of the loan, a fixed interest rate, a variableinterest rate description, a payment amount, a payment schedule, aballoon payment schedule, a collateral specification, a collateralsubstitution description, a description of a party, a guaranteedescription, a guarantor description, a security description, a personalguarantee, a lien, a foreclosure condition, a default condition, aconsequence of default, a principal amount of debt, a balance of debt, afixed interest rate, a variable interest rate, a payment amount, apayment schedule, a balloon payment schedule, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default, a covenant related to any one of the foregoing, and aduration of any one of the foregoing.

An example system may include wherein the valuation circuit comprises avaluation model improvement circuit, wherein the valuation modelimprovement circuit modifies the valuation model based on a first set ofvaluation determinations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security, and wherein the valuation model improvementcircuit comprises at least one system from the list of systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and a hybrid system of at least two of anyof the foregoing.

An example system may include wherein the data collection circuitcomprises at least one system selected from systems consisting of: anInternet of Things system, a camera system, a networked monitoringsystem, an internet monitoring system, a mobile device system, awearable device system, a user interface system, and an interactivecrowdsourcing system.

An example system may include wherein the valuation circuit furthercomprises a collateral classification circuit structured to identify agroup of offset items of collateral, wherein each member of the group ofoffset items of collateral and the item of collateral share a commonattribute, wherein the common attribute is selected from a list ofattributes consisting of: a category of the item of collateral, an ageof the item of collateral, a condition of the item of collateral, ahistory of the item of collateral, an ownership of the item ofcollateral, a caretaker of the item of collateral, a security of theitem of collateral, a condition of an owner of the item of collateral, alien on the item of collateral, a storage condition of the item ofcollateral, a geolocation of the item of collateral, and ajurisdictional location of the item of collateral.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report marketplace information for offset items of collateralrelevant to the value of the item of collateral, wherein the marketvalue data collection circuit is further structured to: monitor one ofpricing or financial data for the offset items of collateral in at leastone public marketplace; and report the monitored one of pricing orfinancial data.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein the item of collateral is selectedfrom the list of items consisting of: a vehicle, a ship, a plane, abuilding, a home, a real estate property, an undeveloped land property,a farm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, a tool, an item of machinery,and an item of personal property.

An example system may further include a blockchain service circuitstructured to store at least one of the smart lending contract or areference to the smart lending contract as blockchain data and areporting circuit structured to report a collateral event based on thereceived data, wherein the collateral event is related to a value of theitem of collateral, a condition of the item of collateral, or anownership of the item of collateral.

An example system may further include an automated agent circuitstructured to perform a collateral-related action in response to thecollateral event, wherein the collateral-related action is selected fromamong the actions consisting of: validating title for the item ofcollateral, recording a change in title for the item of collateral,assessing the value of the item of collateral, initiating inspection ofthe item of collateral, initiating maintenance of the item ofcollateral, initiating security for the item of collateral, andmodifying terms and conditions for the item of collateral.

An example system may include wherein the automated agent circuit isfurther structured to perform a loan-related action in response to thecollateral event, wherein the loan-related action is selected from thelist of actions consisting of: offering the loan, accepting the loan,underwriting the loan, setting an interest rate for a loan, deferring apayment requirement, modifying the interest rate for the loan, callingthe loan, closing the loan, setting terms and conditions for the loan,providing notices required to be provided to a borrower, foreclosing onproperty subject to the loan, and modifying terms and conditions for theloan. In embodiments, provided herein is a method for robotic processautomation of transactional, financial and marketplace activities. Anexample method may include receiving data related to an item ofcollateral that provides security for a loan; determining a value forthe item of collateral based on the received data and a valuation model;creating a smart lending contract, wherein the smart lending contractspecifies a covenant having a required value of collateral; comparingthe value of the item of collateral to the value of collateral specifiedin the covenant; determining a collateral satisfaction value; andimplementing a loan related activity in response to the collateralsatisfaction value.

An example method may further include determining at least one of a termor a condition for the smart lending contract in response to thecollateral satisfaction value; and modifying the smart lending contractto include the at least one of the term or the condition.

An example method may further include modifying the valuation modelbased on a first set of valuation determinations for a first set ofitems of collateral and a corresponding set of loan outcomes having thefirst set of items of collateral as security.

An example method may further include identifying a group of offsetitems of collateral, wherein each member of the group of offset items ofcollateral and the item of collateral share a common attribute, whereinthe common attribute is selected from a list of attributes consistingof: a category of the item of collateral, an age of the item ofcollateral, a condition of the item of collateral, a history of the itemof collateral, an ownership of the item of collateral, a caretaker ofthe item of collateral, a security of the item of collateral, acondition of an owner of the item of collateral, a lien on the item ofcollateral, a storage condition of the item of collateral, a geolocationof the item of collateral, and a jurisdictional location of the item ofcollateral.

An example method may further include monitoring and reportingmarketplace information for data relevant to a member of the group ofoffset items of collateral and modifying the smart lending contract inresponse to the marketplace information, wherein monitoring marketplaceinformation comprises monitoring at least one public marketplace forpricing data or financial data related to the member of the group ofoffset items of collateral.

An example method may further include automatic initiation of a loanrelated action in response to one of the pricing data or the financialdata, wherein the loan-related action includes an action selected from alist of actions consisting of: modifying a term of the loan, issuing anotice of default, initiating a foreclosure action modifying aconditions of the loan, providing a notice to a party of the loan,providing a required notice to a borrower of the loan, and foreclosingon a property subject to the loan.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example platform or system mayinclude a data collection circuit structured to receive data related toa plurality of items of collateral; a collateral classification circuitstructured to identify, among the plurality of items of collateral, atleast one group of related items of collateral, wherein each member ofthe at least one group shares a common attribute; and a smart contractcircuit structured to create a smart lending contract, wherein the smartlending contract defines a subset of items of collateral as security fora set of loans, wherein the subset of items of collateral is selectedfrom the at least one group of related items of collateral.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the collateral classification circuitis further structured to select the common attribute from the receiveddata, wherein the common attribute is a type of the item of collateral,a category of the item of collateral, a value of the item of collateral,a price of a type of the item of collateral, a value of a type of theitem of collateral, a specification of the item of collateral, a productfeature set of the item of collateral, a liquidity of the item ofcollateral, a shelf-life of the item of collateral, a useful life of theitem of collateral, a model of the item of collateral, a brand of theitem of collateral, a manufacturer of the item of collateral, an age ofthe item of collateral, a condition of the item of collateral, avaluation of the item of collateral, a status of the item of collateral,a context of the item of collateral, a state of the item of collateral,a storage location of the item of collateral, a history of the item ofcollateral, an ownership of the item of collateral, a caretaker of theitem of collateral, a security of the item of collateral, a condition ofan owner of the item of collateral, a lien on the item of collateral, astorage condition of the item of collateral, a maintenance history ofthe item of collateral, a usage history of the item of collateral, anaccident history of the item of collateral, a fault history of the itemof collateral, a history of ownership of the item of collateral, anassessment of the item of collateral, a geolocation of the item ofcollateral, a jurisdictional location of the item of collateral, and thelike.

An example system may include wherein the smart lending contract isfurther structured to identify the subset of items of collateral inreal-time, and wherein the common attribute is similarity of status ofthe items of collateral.

An example system may include wherein the similarity of status is basedon each of the subset of items of collateral being in transit during adefined time period.

An example system may include wherein the data collection circuitcomprises at least one system selected from systems consisting of: anInternet of Things system, a camera system, a networked monitoringsystem, an internet monitoring system, a mobile device system, awearable device system, a user interface system, and an interactivecrowdsourcing system.

An example system may include wherein the set of loans comprises aplurality of loans distributed among a plurality of borrowers.

An example system may include wherein a valuation circuit structured todetermine, based on the received data and a valuation model, a value foreach item of collateral in the subset of items of collateral; andwherein the smart contract circuit is further structured to redefine thesubset based on the value for each item of collateral.

An example system may include wherein the smart contract circuit isfurther structured to determine at least one of a term or a conditionfor the smart lending contract based on the value of at least one of thesubset of items of collateral; and modify the smart lending contract toinclude the determined term or condition, wherein the term or thecondition is related to a loan component selected from the loancomponents consisting of: a loan party, a loan collateral, aloan-related event, and a loan-related activity and wherein thedetermined term or condition is a principal amount of the loan, abalance of the loan, a fixed interest rate, a variable interest ratedescription, a payment amount, a payment schedule, a balloon paymentschedule, a collateral specification, a collateral substitutiondescription, a description of a party, a guarantee description, aguarantor description, a security description, a personal guarantee, alien, a foreclosure condition, a default condition, a consequence ofdefault, a covenant related to any one of the foregoing, a duration ofany one of the foregoing, and the like.

An example system may include wherein the valuation circuit comprises avaluation model improvement circuit, wherein the valuation modelimprovement circuit is structured to modify the valuation model based ona first set of valuation determinations for a first set of items ofcollateral and a corresponding set of loan outcomes having the first setof items of collateral as security, wherein the valuation modelimprovement circuit comprises at least one system from the list ofsystems consisting of: a machine learning system, a model-based system,a rule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and a hybrid system including at least twoof the foregoing.

An example system may include wherein the collateral classificationcircuit is further structured to identify a group of offset items ofcollateral, wherein each member of the group of offset items ofcollateral and the subset of items of collateral share a commonattribute.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report marketplace information, such as pricing data and financialdata in at least one public marketplace, for at least one of the groupof offset items of collateral and report the monitored one of pricing orfinancial data.

An example system may include wherein at least one of the set of loansis of a type selected from among the loan types consisting of: an autoloan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

An example system may include wherein, wherein at least one of theplurality of items of collateral is selected from among the list ofitems consisting of: a vehicle, a ship, a plane, a building, a home, areal estate property, an undeveloped land property, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, a tool, an item of machinery, and an item ofpersonal property.

An example system may further include a blockchain service circuit tostore a smart lending contract or a reference to the smart lendingcontract as blockchain data.

An example system may further include a reporting circuit structured toreport a collateral event based on the received data, wherein thecollateral event is related to a value of one of the plurality of itemsof collateral, a condition of one of the plurality of items ofcollateral, or an ownership of one of the plurality of items ofcollateral.

An example system may further include an automated agent circuitstructured to perform a collateral-related action in response to thecollateral event, wherein the collateral-related action is selected fromamong the actions consisting of: validating title for one of theplurality of items of collateral, recording a change in title for one ofthe plurality of items of collateral, assessing the value of one of theplurality of items of collateral, initiating inspection of one of theplurality of items of collateral, initiating maintenance of the one ofthe plurality of items of collateral, initiating security for one of theplurality of items of collateral, and modifying terms and conditions forone of the plurality of items of collateral.

In embodiments, provided herein is a method for transactional, financialand marketplace enablement. An example method may include receiving datarelated to at least one of a plurality of items of collateral;identifying a group of the plurality of items of collateral, whereineach member of the group share a common attribute; identifying a subsetof the group as security of a set of loans; and creating a set of smartlending contracts for the set of loans.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may further include determining a value for each item ofcollateral in the subset of the group using received data and avaluation model.

An example method may further include redefining, based on the value foreach item of collateral in the subset of items of collateral, the subsetof items of collateral used as security for the set of loan, of thegroup.

An example method may further include determining at least one of a termor a condition for at least one of the smart lending contracts based onthe value for at least one of the items of collateral in the subset ofthe group.

An example method may further include modifying the smart lendingcontract to include the at least one of the term and the condition.

An example method may further include modifying the valuation modelbased on a first set of valuation determinations for a first set ofitems of collateral and a corresponding set of loan outcomes having thefirst set of items of collateral as security.

An example method may further include identifying a group of offsetitems of collateral, wherein each member of the group of offset items ofcollateral and the group of the plurality of items of collateral share acommon attribute.

An example method may further include monitoring and reportingmarketplace information for the group of offset items of collateral.

In embodiments, an example platform or system may include a datacollection circuit structured to receive data related to at least one ofa set of parties to a loan; a smart contract circuit structured tocreate a smart lending contract for the loan; and an automated agentcircuit structured to automatically perform a loan-related action inresponse to the received data, wherein the loan-related action is achange in an interest rate for the loan, and wherein the smart contractcircuit is further structured to update the smart lending contract withthe changed interest rate.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the data collection circuit isfurther structured to receive collateral-related data related to a setof items of collateral acting as security for the loan and determine acondition of at least one of the set of items of collateral, wherein thechange in the interest rate is further based on a condition of the atleast one of the set of items of collateral.

An example system may include where in the received data comprises anattribute of the at least one of the set of parties to the loan, andwhere in the change in the interest rate is based in part on theattribute.

An example system may include wherein the smart contract circuit isfurther structured to: determine at least one of a term or a conditionfor the smart lending contract based on the attribute; and modify thesmart lending contract to include the at least one of the term or thecondition.

An example system may include wherein the at least one of the term orthe condition is related to a loan component selected from the loancomponents consisting of: a loan party, a loan collateral, aloan-related event, and a loan-related activity.

An example system may include wherein the at least one of the term orthe condition is selected from the list consisting of: a principalamount of the loan, a balance of the loan, a fixed interest rate, avariable interest rate description, a payment amount, a paymentschedule, a balloon payment schedule, a collateral specification, acollateral substitution description, a description of a party, aguarantee description, a guarantor description, a security description,a personal guarantee, a lien, a foreclosure condition, a defaultcondition, a consequence of default, a covenant related to any one ofthe foregoing, and a duration of any one of the foregoing.

An example system may include wherein the data collection circuitcomprises at least one system selected from systems consisting of: anInternet of Things circuit, an image capture device, a networkedmonitoring circuit, an internet monitoring circuit, a mobile device, awearable device, a user interface circuit, and an interactivecrowdsourcing circuit.

An example system may include wherein the data collection circuitcomprises an Internet of Things circuit structured to monitor attributesof at least one of the set of parties to the loan.

An example system may include wherein the data collection circuitcomprises a wearable device associated with at least one of the set ofparties, and wherein the wearable device is structured to acquirehuman-related data, and wherein the received data includes at least aportion of the human-related data.

An example system may include wherein the data collection circuitcomprises a user interface circuit structured to receive data from atleast one of the parties of the loan and provide the data from at leastone of the parties of the loan as a portion of the received data.

An example system may include wherein the data collection circuitcomprises an interactive crowdsourcing circuit structured to: solicitdata regarding at least one of the set of parties of the loan; receivesolicited data; and provide at least a subset of the solicited data as aportion of the received data.

An example system may include wherein the data collection circuitfurther comprises an internet monitoring circuit structured to retrievedata related to at least one of the parties of the loan from at leastone publicly available information site.

An example system may include further comprising a valuation circuitstructured to determine, based on the received data and a valuationmodel, a value for the at least one of the set of items of collateral.

An example system may include wherein the smart contract circuit isfurther structured to: determine at least one of a term or a conditionfor the smart lending contract based on the value for the at least oneof the set of items of collateral; and modify the smart lending contractto include the at least one of the term or the condition.

An example system may include wherein the at least one of the term orthe condition is related to a loan component selected from the loancomponents consisting of: a loan party, a loan collateral, aloan-related event, and a loan-related activity.

An example system may include wherein the at least one of the term orthe condition is selected from the list consisting of: a principalamount of the loan, a balance of the loan, a fixed interest rate, avariable interest rate description, a payment amount, a paymentschedule, a balloon payment schedule, a collateral specification, acollateral substitution description, a description of a party, aguarantee description, a guarantor description, a security description,a personal guarantee, a lien, a foreclosure condition, a defaultcondition, a consequence of default, a covenant related to any one ofthe foregoing, and a duration of any one of the foregoing.

An example system may include wherein the valuation circuit comprises avaluation model improvement circuit, wherein the valuation modelimprovement circuit modifies the valuation model based on a first set ofvaluation determinations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security.

An example system may include wherein the valuation model improvementcircuit comprises at least one system from the list of systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and a hybrid system including at least twoof the foregoing.

An example system may include wherein the change in the interest rate isfurther based on the value for the at least one of the set of items ofcollateral.

An example system may include further comprising a collateralclassification circuit structured to identify a group of offset items ofcollateral, wherein each member of the group of offset items ofcollateral and at least one of the set of items of collateral share acommon attribute.

An example system may include wherein the common attribute is selectedfrom a list of attributes consisting of: a category of the item, an ageof the item, a condition of the item, a history of the item, anownership of the item, a caretaker of the item, a security of the item,a condition of an owner of the item, a lien on the item, a storagecondition of the item, a geolocation of the item, and a jurisdictionallocation of the item.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report marketplace information for offset items of collateralrelevant to the value of the item of collateral.

An example system may include wherein the market value data collectioncircuit is further structured to: monitor one of pricing or financialdata for the offset items of collateral in at least one publicmarketplace; and report the monitored one of pricing or financial data.

An example system may include wherein the item of collateral is selectedfrom the list of items consisting of: a vehicle, a ship, a plane, abuilding, a home, a real estate property, an undeveloped land property,a farm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, a tool, an item of machinery,and an item of personal property.

An example system may include wherein the loan is of a type selectedfrom among the loan types consisting of: an auto loan, an inventoryloan, a capital equipment loan, a bond for performance, a capitalimprovement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

In embodiments, an example method may include receiving data related toat least one of a set of parties to a loan; creating a smart lendingcontract for the loan; performing a loan-related action in response tothe received data, wherein the loan-related action is a change in aninterest rate for the loan; and updating the smart lending contract withthe changed interest rate.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may include further comprising: receiving data related toa set of items of collateral acting as security for the loan;determining a condition of at least one of the set of items ofcollateral; and performing a loan-related action in response to thecondition of the at least one of the set of items of collateral, whereinthe loan-related action is a change in interest rate for the loan.

An example method may include receiving data related to a set of itemsof collateral acting as security for the loan; determining a conditionof at least one of the set of items of collateral; determining at leastone of a term or a condition for the smart lending contract based on thecondition of the at least one of the set of items of collateral; andmodifying the smart lending contract to include the at least one of theterm or the condition.

An example method may include identifying a group of offset items ofcollateral wherein each member of the group of offset items ofcollateral and at least one of the set of items of collateral share acommon attribute; monitoring the group of offset items of collateral inat least one public marketplace; and reporting monitored data.

An example method may include further comprising changing, based atleast in part on the monitored group of offset items of collateral, theinterest rate of the loan secured by at least one of the set of items ofcollateral.

In embodiments, an example platform or system may include a datacollection circuit structured to acquire data, from public sources ofinformation, related to at least one party of a set of parties to aloan; a smart contract circuit structured to create a smart lendingcontract for the loan; and an automated agent circuit structured toautomatically perform a loan-related action in response to the acquireddata, wherein the loan-related action is a change in an interest ratefor the loan, and wherein the smart contract circuit is furtherstructured to update the smart lending contract with the changedinterest rate.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the public sources of informationinclude at least one information source selected from the sourcesconsisting of: a website, a news article, a social network, andcrowdsourced information.

An example system may include wherein the acquired data comprises afinancial condition of the at least one party of the set of parties tothe loan.

An example system may include wherein the financial condition isdetermined based on at least one attribute of the at least one party ofthe set of parties to the loan, the attribute selected from among thelist of attributes consisting of: a publicly stated valuation of theparty, a set of property owned by the party as indicated by publicrecords, a valuation of a set of property owned by the party, abankruptcy condition of the party, a foreclosure status of the party, acontractual default status of the party, a regulatory violation statusof the party, a criminal status of the party, an export controls statusof the party, an embargo status of the party, a tariff status of theparty, a tax status of the party, a credit report of the party, a creditrating of the party, a website rating of the party, a set of customerreviews for a product of the party, a social network rating of theparty, a set of credentials of the party, a set of referrals of theparty, a set of testimonials for the party, a set of behavior of theparty, a location of the party, a geolocation of the party, and ajudicial location of the party.

An example system may include wherein the at least one party is selectedfrom a list of parties consisting of: a primary lender, a secondarylender, a lending syndicate, a corporate lender, a government lender, abank lender, a secured lender, bond issuer, a bond purchaser, anunsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, and an accountant.

An example system may include wherein the data collection circuit isfurther structured to receive collateral-related data related to a setof items of collateral acting as security for the loan and to determinea condition of at least one of the set of items of collateral, whereinthe change in the interest rate is further based on the condition of theat least one of the set of items of collateral.

An example system may include further comprising an automated agentcircuit structured to identify an event relevant to the loan, based, atleast in part, on the received data.

An example system may include wherein the event relevant to the loancomprises an event relevant to at least one of: a value of the loan, acondition of collateral of the loan, or an ownership of collateral ofthe loan.

An example system may include wherein the automated agent circuit isfurther structured to perform, in response to the event relevant to theloan, an action selected from the list of actions consisting of:offering the loan, accepting the loan, underwriting the loan, setting aninterest rate for the loan, deferring a payment requirement, modifyingan interest rate for the loan, validating title for at least one of theset of items of collateral, assessing the value of at least one of theset of items of collateral, initiating inspection of at least one of theset of items of collateral, setting or modifying terms and conditionsfor the loan, providing a notice to one of the parties, providing arequired notice to a borrower of the loan, and foreclosing on a propertysubject to the loan.

An example system may include wherein the smart contract circuit isfurther structured to specify terms and conditions in the smart lendingcontract, wherein one of a term or a condition in the smart lendingcontract governs one of loan-related events or loan-related activities.

An example system may include wherein the terms and conditions are eachselected from the list consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, a party,a guarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default.

An example system may include wherein the loan comprises a loan typeselected from the loan types consisting of: an auto loan, an inventoryloan, a capital equipment loan, a bond for performance, a capitalimprovement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein the acquired data is related toone of the set of items of collateral selected from the list consistingof: a vehicle, a ship, a plane, a building, a home, a real estateproperty, an undeveloped land property, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,a tool, an item of machinery, and an item of personal property.

An example system may include further comprising a valuation circuitstructured to determine, based on the acquired data and a valuationmodel, a value for at least one of the set of items of collateral.

An example system may include wherein the smart contract circuit isfurther structured to: determine at least one of a term or a conditionfor the smart lending contract based on the value for the at least oneof the set of items of collateral; and modify the smart lending contractto include the at least one of the term or the condition.

An example system may include wherein the valuation circuit comprises avaluation model improvement circuit, wherein the valuation modelimprovement circuit modifies the valuation model based on a first set ofvaluation determinations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security.

An example system may include wherein the valuation model improvementcircuit comprises at least one system from the list of systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a neural network, aconvolutional neural network, a feed forward neural network, a feedbackneural network, a self-organizing map, a fuzzy logic system, a randomwalk system, a random forest system, a probabilistic system, a Bayesiansystem, a simulation system, and a hybrid system including at least twoof the foregoing.

An example system may include further comprising a collateralclassification circuit structured to identify a group of offset items ofcollateral, wherein each member of the group of offset items ofcollateral and at least one of the set of items of collateral share acommon attribute.

An example system may include wherein the common attribute is selectedfrom a list of attributes consisting of: a category of the item, an ageof the item, a condition of the item, a history of the item, anownership of the item, a caretaker of the item, a security of the item,a condition of an owner of the item, a lien on the item, a storagecondition of the item, a geolocation of the item o, and a jurisdictionallocation of the item.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report marketplace information for offset items of collateralrelevant to the value of the item of collateral.

An example system may include wherein the market value data collectioncircuit is further structured to: monitor one of pricing or financialdata for the offset items of collateral in at least one publicmarketplace; and report the monitored one of pricing or financial data.

An example system may include wherein the smart contract circuit isfurther structured to modify a term or condition of the loan based onthe marketplace information for offset items of collateral relevant tothe value of the item of collateral.

In embodiments, an example method may include acquiring data, frompublic sources, related to at least one of a set of parties to a loan,wherein the public sources of information are selected from the list ofinformation sources consisting of: a website, a news article, a socialnetwork, and crowdsourced information; creating a smart lendingcontract; performing a loan-related action in response to the acquireddata, wherein the loan-related action is a change in an interest ratefor the loan; and updating the smart lending contract with the changedinterest rate.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may include receiving collateral-related data related toa set of items of collateral acting as security for the loan; anddetermining a condition of at least one of the set of items ofcollateral, wherein the change in the interest rate is further based onthe condition of the at least one of the set of items of collateral.

An example method may include identifying an event relevant to the loanbased, at least in part, on the collateral-related data; and performing,in response the event relevant to the loan, an action selected from thelist of actions consisting of: offering the loan, accepting the loan,underwriting the loan, setting an interest rate for the loan, deferringa payment requirement, modifying an interest rate for the loan,validating title for at least one of the set of items of collateral,assessing a value of at least one of the set of items of collateral,initiating inspection of at least one of the set of items of collateral,setting or modifying terms and conditions for the loan, providing anotice to one of the parties, providing a required notice to a borrowerof the loan, and foreclosing on a property subject to the loan.

An example method may include further comprising determining, based onat least one of the collateral-related data or the acquired data, and avaluation model, a value for at least one of the set of items ofcollateral.

An example method may include further comprising determining at leastone of a term or a condition for the smart lending contract based on thevalue for the at least one of the set of items of collateral.

An example method may include further comprising modifying the smartlending contract to include the at least one of the term or thecondition.

An example method may include further comprising modifying the valuationmodel based on a first set of valuation determinations for a first setof items of collateral and a corresponding set of loan outcomes havingthe first set of items of collateral as security.

An example method may include identifying a group of offset items ofcollateral, wherein each member of the group of offset items ofcollateral and at least one of the set of items of collateral share acommon attribute; monitoring one of pricing data or financial data forleast one of the group offset items of collateral in at least one publicmarketplace; reporting the monitored data for the at least one of thegroup offset items of collateral; and modifying a term or condition ofthe loan based the reported monitored data.

In embodiments, an example platform or system may include a datacollection circuit structured to receive data relating to a status of aloan and data relating to a set of items of collateral acting assecurity for the loan; a blockchain service circuit structured tomaintain a secure historical ledger of events related to the loan, theblock chain circuit further structured to interpret a plurality ofaccess control features corresponding to a plurality of partiesassociated with the loan; a loan evaluation circuit structured todetermine a loan status based on the received data; a smart contractcircuit structured to create a smart lending contract for the loan; andan automated agent circuit structured to perform a loan-action based onthe loan status; wherein the blockchain service circuit is furtherstructured to update the historical ledger of events with the loanaction.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the data collection circuit isfurther structured to receive data related to one or more loan entities,and wherein the loan evaluation circuit is further structured todetermine compliance with a covenant based on the data related to theone or more of the loan entities.

An example system may include wherein the data collection circuitfurther comprises at least one system for monitoring one or more of theloan entities, the system selected from the systems consisting of: anInternet of Things system, a camera system, a networked monitoringsystem, an internet monitoring system, a mobile device system, awearable device system, a user interface system, and an interactivecrowdsourcing system.

An example system may include wherein the interactive crowdsourcingsystem comprises a user interface, the user interface configured tosolicit information related to one or more of the loan entities from acrowdsourcing site.

An example system may include wherein the user interface is structuredto allow one or more of the loan entities to input information one ormore of the loan entities.

An example system may include wherein the networked monitoring systemcomprises a network search circuit structured to search publiclyavailable information sites for information related one or more of theloan entities.

An example system may include wherein the loan evaluation circuit isfurther structured to determine a state of performance for a conditionof the loan based on the received data and a status of the one or moreof the loan entities, and wherein the determination of the loan statusis determined based in part on the status of the at least one or more ofthe loan entities and the state of performance of the condition for theloan.

An example system may include wherein the condition of the loan relatesto at least one of a payment performance and a satisfaction on acovenant.

An example system may include wherein the data collection circuitfurther comprises a market data collection circuit structured to receivefinancial data regarding at least one of the plurality of partiesassociated with the loan.

An example system may include wherein the loan evaluation circuit isfurther structured to determine a financial condition of the least oneof the plurality of parties associated with the loan based on thereceived financial data.

An example system may include wherein the at least one of the pluralityof parties is selected from a list of parties consisting of: a primarylender, a secondary lender, a lending syndicate, a corporate lender, agovernment lender, a bank lender, a secured lender, bond issuer, a bondpurchaser, an unsecured lender, a guarantor, a provider of security, aborrower, a debtor, an underwriter, an inspector, an assessor, anauditor, a valuation professional, a government official, and anaccountant.

An example system may include wherein the received financial datarelates to an attribute of the entity at least one of the plurality ofparties selected from the list of attributes consisting of: a publiclystated valuation of the party, a set of property owned by the party asindicated by public records, a valuation of a set of property owned bythe party, a bankruptcy condition of the party, a foreclosure status ofthe entity, a contractual default status of the entity, a regulatoryviolation status of the entity, a criminal status of the entity, anexport controls status of the entity, an embargo status of the entity, atariff status of the entity, a tax status of the entity, a credit reportof the entity, a credit rating of the entity, a website rating of theentity, a set of customer reviews for a product of the entity, a socialnetwork rating of the entity, a set of credentials of the entity, a setof referrals of the entity, a set of testimonials for the entity, a setof behavior of the entity, a location of the entity, and a geolocationof the entity.

An example system may include further comprising a valuation circuitstructured to determine, based on the received data and a valuationmodel, a value for at least one of the set of items of collateral.

An example system may include wherein the smart contract circuit isfurther structured to determine at least one of a term or a conditionfor the smart lending contract based on the value for the at least oneof the set of items of collateral; and modify the smart lending contractto include the at least one of the term or the condition.

An example system may include wherein the terms and conditions are eachselected from the list consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, a party,a guarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default.

An example system may include wherein the valuation circuit comprises avaluation model improvement circuit, wherein the valuation modelimprovement circuit modifies the valuation model based on a first set ofvaluation determinations for a first set of items of collateral and acorresponding set of loan outcomes having the first set of items ofcollateral as security.

An example system may include wherein the valuation model improvementcircuit comprises at least one system from the list of systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network, a self-organizing map, a fuzzy logic system,a random walk system, a random forest system, a probabilistic system, aBayesian system, and a simulation system.

An example system may include further comprising a collateralclassification circuit structured to identify a group of offset items ofcollateral, wherein each member of the group of offset items ofcollateral and at least one of the set of items of collateral share acommon attribute.

An example system may include wherein the common attribute is selectedfrom a list of attributes consisting of: a category of the item ofcollateral, an age of the item of collateral, a condition of the item ofcollateral, a history of the item of collateral, an ownership of theitem of collateral, a caretaker of the item of collateral, a security ofthe item of collateral, a condition of an owner of the item ofcollateral, a lien on the item of collateral, a storage condition of theitem of collateral, a geolocation of the item of collateral, and ajurisdictional location of the item of collateral.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report marketplace information for offset items of collateralrelevant to the value of the item of collateral.

An example system may include wherein the market value data collectioncircuit is further structured to monitor one of pricing or financialdata for the offset items of collateral in at least one publicmarketplace; and report the monitored one of pricing or financial data.

An example system may include wherein the smart contract circuit isfurther structured to modify a term or condition of the loan based onthe marketplace information for offset items of collateral relevant tothe value of the item of collateral.

In embodiments, an example method may include maintaining a securehistorical ledger of events related to a loan; receiving data relatingto a status of the loan; receiving data related to a set of items ofcollateral acting as security of the loan; determining a status of theloan; performing a loan-action based on the loan status; and updatingthe historical ledger of events related to the loan.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may include receiving data related to one or more loanentities; and determining compliance with a covenant of the loan basedon the data received.

An example method may include determining a state of performance for acondition of the loan, wherein the determination of the loan status isbased on part on the state of performance of the condition of the loan.

An example method may include receiving financial data related to atleast one party to the loan.

An example method may include determining a financial condition of theat least one party to the loan based on the financial data.

An example method may include determining a value for at least one setof items of collateral based on the received data and a valuation model.

An example method may include determining at least one of a term or acondition for the loan based on the value of the at least one of theitems of collateral; and modifying a smart lending contract to includethe at least one of the term or the condition.

An example method may include identifying a group of offset items ofcollateral, wherein each member of the group of offset items ofcollateral and at least one of the set of items of collateral share acommon attribute; receiving data related to the group of offset items ofcollateral, wherein the determination of the value for the at least oneset of items of collateral is partially based on the received datarelated to the group of offset items of collateral.

In embodiments, provided herein is a smart contract system for managingcollateral for a loan. An example platform, system, or apparatus mayinclude a data collection circuit structured to monitor a status of aloan and of a collateral for the loan; a smart contract circuitstructured to process information from the data collection circuit andautomatically initiate at least one of a substitution, a removal, or anaddition of one or items from the collateral for the loan based on theinformation and a smart lending contract in response to at least one ofthe status of the loan or the status of the collateral for the loan; anda blockchain service circuit structured to interpret a plurality ofaccess control features corresponding to at least one party associatedwith the loan and record the at least one substitution, removal, oraddition in a distributed ledger for the loan.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the data collection circuit furtherincludes at least one system selected from the systems consisting of: anInternet of Things system, a camera system, a networked monitoringsystem, an internet monitoring system, a mobile device system, awearable device system, a user interface system, and an interactivecrowdsourcing system.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein a status of the loan is determinedbased on the status of at least one of an entity related to the loan anda state of a performance of a condition for the loan.

An example system may include wherein the state of the performance ofthe condition relates to at least one of a payment performance or asatisfaction of a covenant for the loan.

An example system may include wherein the status of the loan isdetermined based on a status of at least one entity related to the loanand a state of performance of a condition for the loan; wherein theperformance of the condition relates to at least one of a paymentperformance or a satisfaction of a covenant for the loan; and whereinthe data collection circuit is further structured to determinecompliance with the covenant by monitoring the at least one entity.

An example system may include wherein the at least one entity is a partyto the loan, and wherein the data collection circuit is furtherstructured to monitor a financial condition of the at least one entity.

An example system may include wherein the condition for the loancomprises a financial condition for the loan, and wherein the state ofperformance of the financial condition is determined based on anattribute selected from the attributes consisting of: a publicly statedvaluation of the at least one entity, a property owned by the at leastone entity as indicated by public records, a valuation of a propertyowned by the at least one entity, a bankruptcy condition of the at leastone entity, a foreclosure status of the at least one entity, acontractual default status of the at least one entity, a regulatoryviolation status of the at least one entity, a criminal status of the atleast one entity, an export controls status of the at least one entity,an embargo status of the at least one entity, a tariff status of the atleast one entity, a tax status of the at least one entity, a creditreport of the at least one entity, a credit rating of the at least oneentity, a website rating of the at least one entity, a plurality ofcustomer reviews for a product of the at least one entity, a socialnetwork rating of the at least one entity, a plurality of credentials ofthe at least one entity, a plurality of referrals of the at least oneentity, a plurality of testimonials for the at least one entity, abehavior of the at least one entity, a location of the at least oneentity, a geolocation of the at least one entity, and a relevantjurisdiction for the at least one entity.

An example system may include wherein the party to the loan comprises atleast one party selected from the parties consisting of: a primarylender, a secondary lender, a lending syndicate, a corporate lender, agovernment lender, a bank lender, a secured lender, bond issuer, a bondpurchaser, an unsecured lender, a guarantor, a provider of security, aborrower, a debtor, an underwriter, an inspector, an assessor, anauditor, a valuation professional, a government official, and anaccountant.

An example system may include wherein the data monitoring circuit isfurther structured to monitor the status of the collateral of the loanbased on at least one attribute of the collateral selected from theattributes consisting of: a category of the collateral, an age of thecollateral, a condition of the collateral, a history of the collateral,a storage condition of the collateral, and a geolocation of thecollateral.

An example system may include wherein the collateral comprises at leastone item selected from the items consisting of: a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

An example system may further include a valuation circuit structured usea valuation model to determine a value for the collateral based on thestatus of the collateral for the loan.

An example system may include wherein the smart contract circuit isfurther structured to initiate the at least one substitution, removal,or addition of one or more items from the collateral for the loan tomaintain a value of the collateral within a predetermined range.

An example system may include wherein the valuation circuit furthercomprises a transactions outcome processing circuit structured tointerpret outcome data relating to a transaction in collateral anditeratively improve the valuation model in response to the outcome data.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report on marketplace information relevant to a value of thecollateral.

An example system may include wherein the market value data collectioncircuit is further structured to monitor at least one of pricing data orfinancial data for an offset collateral item in at least one publicmarketplace.

An example system may include wherein the market value data collectioncircuit is further structured to construct a set of offset collateralitems for valuing the item of collateral using a clustering circuitbased on an attribute of the collateral.

An example system may include wherein the attribute comprises at leastone attribute selected from among: a category of the collateral, an ageof the collateral, a condition of the collateral, a history of thecollateral, a storage condition of the collateral, and a geolocation ofthe collateral.

An example system may include wherein the smart lending contractcomprises terms and conditions for the loan, wherein each of the termsand conditions comprise at least one member selected from the groupconsisting of: a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

An example system may include wherein the smart contract circuit furthercomprises a loan management circuit structured to specify terms andconditions of the smart lending contract that governs at least one of:terms and conditions of the loan, a loan-related event, or aloan-related activity.

In embodiments, provided herein is a smart contract method for managingcollateral for a loan. An example method may include monitoring a statusof a loan and of a collateral for the loan; processing information fromthe monitoring and automatically initiating at least one of asubstitution, a removal, or an addition of one or more items from thecollateral for the loan based on the at least one of the status of theloan or the collateral for the loan; and interpreting a plurality ofaccess control features corresponding to at least one party associatedwith the loan and recording the at least one substitution, removal, oraddition in a distributed ledger for the loan.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments.

An example method may include wherein the status of the loan isdetermined based on a status of at least one of an entity related to theloan or a state of a performance of a condition for the loan.

An example method may include determining a value with a valuation modelfor a set of collateral based on at least one of the status of the loanor the collateral for the loan.

An example method may include wherein the at least one substitution,removal, or addition is initiated to maintain a value of the collateralwithin a predetermined range.

An example method may include interpreting outcome data relating to atransaction of one of the collateral or an offset collateral anditeratively improving the valuation model in response to the outcomedata.

An example method may include monitoring and reporting on marketplaceinformation relevant to a value of the collateral.

An example method may include monitoring at least one of pricing data orfinancial data for an offset collateral item in at least one publicmarketplace.

An example method may include specifying terms and conditions of a smartcontract that governs at least one of terms and conditions for the loan,a loan-related event, or a loan-related activity.

An example apparatus may include a data collection circuit structured tomonitor at least one of a status of a loan or a status of a collateralfor the loan; a smart contract circuit structured interpret a smartcontract for the loan, and to adjust at least one term or condition ofthe smart contract for the loan in response to the at least one of thestatus of the loan or the status of the collateral for the loan; and ablockchain service circuit structured to interpret a plurality of accesscontrol features corresponding to a plurality of parties associated withthe loan and record the adjusted at least one term or condition of thesmart contract for the loan in a distributed ledger for the loan. Thedata collection circuit may monitor the status of the collateral for theloan, the apparatus further including a valuation circuit structured usea valuation model to determine a value for the collateral based on thestatus of the collateral for the loan, and wherein the smart contractcircuit is further structured to adjust at least one term or conditionof the smart contract for the loan in response to the value for thecollateral.

In embodiments, provided herein is a crowdsourcing system for validatingconditions of collateral for a loan. An example platform, system, orapparatus may include a crowdsourcing request circuit structured toconfigure at least one parameter of a crowdsourcing request related toobtaining information on a condition of a collateral for a loan; acrowdsourcing publishing circuit configured to publish the crowdsourcingrequest to a group of information suppliers; and a crowdsourcingcommunications circuit structured to collect and process at least oneresponse from the group of information suppliers, and to provide areward to at least one of the group of information suppliers in responseto a successful information supply event. A successful informationsupply event may be the receipt of information identified to relate to acollateral that is the subject of the crowdsourcing request and whereinthe information relates to a condition of the collateral. Informationregarding identifying features of the collateral, such as a serialnumber or a model number, may not be a successful information supplyevent.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the crowdsourcing publishing circuitis further configured to publish a reward description to at least aportion of the group of information suppliers in response to thesuccessful information supply event. The reward description may includea kind or type of reward, a value of the reward, an amount of thereward, information regarding valid dates of use of the reward orinformation for using the reward, and the like.

An example system may include wherein the crowdsourcing communicationscircuit further includes or is in communication with a smart contractcircuit structured to manage the reward by determining the successfulinformation supply event in response to the at least one parameterconfigured for the crowdsourcing request, and to automatically allocatethe reward to the at least one of the group of information suppliers inresponse to the successful information supply event.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein the collateral comprises at leastone item selected from the items consisting of: a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

An example system may include wherein the condition of collateral isdetermined based on an attribute selected from the attributes consistingof: a quality of the collateral, a condition of the collateral, a statusof a title to the collateral, a status of a possession of thecollateral, and a status of a lien on the collateral.

An example system may include wherein the condition of the collateral,wherein the collateral is an item, is determined based on an attributeselected from the attributes consisting of: a new or used status of theitem, a type of the item, a category of the item, a specification of theitem, a product feature set of the item, a model of the item, a brand ofthe item, a manufacturer of the item, a status of the item, a context ofthe item, a state of the item, a value of the item, a storage locationof the item, a geolocation of the item, an age of the item, amaintenance history of the item, a usage history of the item, anaccident history of the item, a fault history of the item, an ownershipof the item, an ownership history of the item, a price of a type of theitem, a value of a type of the item, an assessment of the item, and avaluation of the item.

An example system may further include a blockchain service circuitstructured to record identifying information and the at least oneparameter of the crowdsourcing request, the at least one response to thecrowdsourcing request, and a reward description in a distributed ledgerfor the crowdsourcing request.

An example system may include wherein the crowdsourcing request circuitis further structured to enable a workflow by which a human user entersthe at least one parameter to establish the crowdsourcing request.

An example system may include wherein the at least one parametercomprises a type of requested information, a reward description, and acondition for receiving the reward.

An example system may include wherein the reward is selected fromselected from the rewards consisting of: a financial reward, a token, aticket, a contractual right, a cryptocurrency amount, a plurality ofreward points, a currency amount, a discount on a product or service,and an access right.

An example system may further include a smart contract circuitstructured to process the at least one response and, in response,automatically undertake an action related to the loan.

An example system may include wherein the action is at least one of aforeclosure action, a lien administration action, an interest-ratesetting action, a default initiation action, a substitution ofcollateral, or a calling of the loan.

An example system may further include a robotic process automationcircuit structured to, based on training on a training data setcomprising human user interactions with at least one of thecrowdsourcing request circuit or the crowdsourcing communicationscircuit, configure the crowdsourcing request based on at least oneattribute of the loan.

An example system may include wherein the at least one attribute of theloan is obtained from a smart contract circuit that manages the loan.

An example system may include wherein the training data set furthercomprises outcomes from a plurality of crowdsourcing requests.

An example system may include wherein the robotic process automationcircuit is further structured to determine the reward.

An example system may include wherein the robotic process automationcircuit is further structured to determine at least one domain to whichthe crowdsourcing publishing circuit publishes the crowdsourcingrequest.

In embodiments, provided herein is a crowdsourcing method for validatingconditions of collateral for a loan. An example method may includeconfiguring at least one parameter of a crowdsourcing request related toobtaining information on a condition of a collateral for a loan;publishing the crowdsourcing request to a group of informationsuppliers; collecting and processing at least one response to thecrowdsourcing request; and providing a reward in response to asuccessful information supply event.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments.

An example method may further include publishing a reward description toat least a portion of the group of information suppliers in response tothe successful information supply event.

An example method may further include wherein the reward isautomatically allocated to at least one of the group of informationsuppliers in response to the successful information supply event.

An example method may further include recording identifying informationand the at least one parameter of the crowdsourcing request, the atleast one response to the crowdsourcing request, and a rewarddescription, in a distributed ledger for the crowdsourcing request.

An example method may further include configuring a graphical userinterface to enable a workflow by which a human user enters the at leastone parameter to establish the crowdsourcing request.

An example method may further include automatically undertaking anaction related to the loan in response to the successful informationsupply event.

An example method may further include training a robotic processautomation circuit on a training data set comprising a plurality ofoutcomes corresponding to a plurality of the crowdsourcing requests, andoperating the robotic process automation circuit to iteratively improvethe crowdsourcing request.

An example method may further include providing at least one attributeof the loan to the robotic process automation circuit to configure thecrowdsourcing request.

An example method may further include configuring the crowdsourcingrequest comprises determining the reward.

An example method may further include inputting at least one attributeof the loan to the robotic process automation circuit to determine atleast one domain to which to publish the crowdsourcing request.

An example apparatus may include a crowdsourcing request circuitstructured to provide an interface to enable configuration of at leastone parameter of a crowdsourcing request related to obtaininginformation on a condition of a collateral for a loan; a crowdsourcingpublishing circuit configured to publish the crowdsourcing request to agroup of information suppliers in response to the crowdsourcing request;and a crowdsourcing communications circuit structured to provide aninterface to collect at least one response to the crowdsourcing requestfrom members of the group of information suppliers, and to provide areward to at least one of the group of information suppliers in responseto a successful information supply event.

The apparatus may further include a smart contract circuit structured tomanage the reward by determining the successful information supply eventin response to the at least one parameter configured for thecrowdsourcing request, and to automatically allocate the reward to theat least one of the group of information suppliers in response to thesuccessful information supply event.

In embodiments, provided herein is a crowdsourcing system for validatingconditions of a guarantor for a loan. An example platform, system, orapparatus may include a crowdsourcing request circuit structured toconfigure at least one parameter of a crowdsourcing request related toobtaining information on a condition of a guarantor for a loan; acrowdsourcing publishing circuit configured to publish the crowdsourcingrequest to a group of information suppliers; and a crowdsourcingcommunications circuit structured to collect and process at least oneresponse from the group of information suppliers, and to provide areward to at least one of the group of information suppliers in responseto a successful information supply event.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the condition is a financialcondition of an entity that is the guarantor for the loan. An examplesystem may include wherein the financial condition is determined atleast in part based on information about the entity selected from theinformation consisting of: a publicly stated valuation of the entity, aproperty owned by the entity as indicated by a public record, avaluation of a property owned by the entity, a bankruptcy condition ofthe entity, a foreclosure status of the entity, a contractual defaultstatus of the entity, a regulatory violation status of the entity, acriminal status of the entity, an export controls status of the entity,an embargo status of the entity, a tariff status of the entity, a taxstatus of the entity, a credit report of the entity, a credit rating ofthe entity, a web site rating of the entity, a plurality of customerreviews for a product of the entity, a social network rating of theentity, a plurality of credentials of the entity, a plurality ofreferrals of the entity, a plurality of testimonials for the entity, aplurality of behaviors of the entity, a location of the entity, ageolocation of the entity, and a jurisdiction of the entity.

The crowdsourcing communications circuit may further include a smartcontract circuit structured to manage the reward by determining thesuccessful information supply event in response to the at least oneparameter configured for the crowdsourcing request, and to automaticallyallocate the reward to the at least one of the group of informationsuppliers in response to the successful information supply event.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein the crowdsourcing request circuitis further structured to configure at least one further parameter of thecrowdsourcing request to obtain information on a condition of acollateral for the loan.

An example system may include wherein the collateral comprises at leastone item selected from the items consisting of: a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

An example system may include wherein the condition of the collateral,wherein the collateral is an item, and wherein the condition of thecollateral is determined based on an attribute selected from theattributes consisting of: a new or used status of the item, a type ofthe item, a category of the item, a specification of the item, a productfeature set of the item, a model of the item, a brand of the item, amanufacturer of the item, a status of the item, a context of the item, astate of the item, a value of the item, a storage location of the item,a geolocation of the item, an age of the item, a maintenance history ofthe item, a usage history of the item, an accident history of the item,a fault history of the item, an ownership of the item, an ownershiphistory of the item, a price of a type of the item, a value of a type ofthe item, an assessment of the item, and a valuation of the item.

An example system may further include a blockchain service circuitstructured to record identifying information and the at least oneparameter of the crowdsourcing request, the at least one response to thecrowdsourcing request, and a reward description in a distributed ledgerfor the crowdsourcing request.

An example system may include wherein the crowdsourcing request circuitis further structured to enable a workflow by which a human user entersthe at least one parameter to establish the crowdsourcing request.

An example system may include wherein the at least one parametercomprises a type of requested information, a reward description, and acondition for receiving the reward.

An example system may include wherein the reward is selected fromselected from the rewards consisting of: a financial reward, a token, aticket, a contractual right, a cryptocurrency amount, a plurality ofreward points, a currency amount, a discount on a product or service,and an access right.

An example system may further include a smart contract circuitstructured to process the at least one response and, in response,automatically undertake an action related to the loan.

An example system may include a smart contract circuit structured toprocess the at least one response and, in response, automaticallyundertake an action related to the loan, wherein the action is at leastone of a foreclosure action, a lien administration action, aninterest-rate setting action, a default initiation action, asubstitution of collateral, and a calling of the loan.

An example system may further include a robotic process automationcircuit structured to, based on training on a training data setcomprising human user interactions with at least one of thecrowdsourcing request circuit or the crowdsourcing communicationscircuit, to configure a crowdsourcing request based on at least oneattribute of a loan.

An example system may include wherein the at least one attribute of theloan is obtained from a smart contract circuit that manages the loan

An example system may include wherein the training data set furthercomprises outcomes from a plurality of crowdsourcing requests.

An example system may include wherein the robotic process automationcircuit is further structured to determine a reward.

An example system may include wherein the robotic process automationcircuit is further structured to determine at least one domain to whichthe crowdsourcing publishing circuit publishes the crowdsourcingrequest.

In embodiments, provided herein is a crowdsourcing method for validatingconditions of collateral for a loan. An example method may includeconfiguring at least one parameter of a crowdsourcing request related toobtaining information on a condition of a guarantor for a loan;publishing the crowdsourcing request to a group of informationsuppliers; collecting and processing at least one response to thecrowdsourcing request; and providing a reward to at least one supplierof the group of information suppliers in response to a successfulinformation supply event.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may further include publishing a reward description to atleast a portion of the group of information suppliers in response to thesuccessful information supply event.

An example method may further include wherein the reward isautomatically allocated to at least one of the group of informationsuppliers in response to the successful information supply event.

An example method may further include recording identifying informationand the at least one parameter of the crowdsourcing request, the atleast one response to the crowdsourcing request, and a rewarddescription, in a distributed ledger for the crowdsourcing request.

An example method may further include configuring a graphical userinterface to enable a workflow by which a human user enters the at leastone parameter to establish the crowdsourcing request.

An example method may further include automatically undertaking anaction related to the loan in response to the successful informationsupply event.

An example method may further include training a robotic processautomation circuit on a training data set comprising a plurality ofoutcomes corresponding to a plurality of the crowdsourcing requests, andoperating the robotic process automation circuit to iteratively improvethe crowdsourcing request.

An example method may further include providing at least one attributeof the loan to the robotic process automation circuit in order toconfigure the crowdsourcing request.

An example method may further include configuring the crowdsourcingrequest comprises determining the reward.

An example method may further include inputting at least one attributeof the loan to the robotic process automation circuit to determine atleast one domain to which to publish the crowdsourcing request.

In embodiments, provided herein is a smart contract system for modifyinga loan having a set of computational services. An example platform,system, or apparatus may include a data collection circuit structured todetermine location information corresponding to each one of a pluralityof entities involved in a loan; a jurisdiction definition circuitstructured to determine a jurisdiction for at least one of the pluralityof entities in response to the location information; and a smartcontract circuit structured to automatically undertake a loan-relatedaction for the loan based at least in part on the jurisdiction for atleast one of the plurality of entities.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the smart contract circuit is furtherstructured to automatically undertake the loan-related action inresponse to a first one of the plurality of entities being in a firstjurisdiction, and a second one of the plurality of entities being in asecond jurisdiction.

An example system may include wherein the smart contract circuit isfurther structured to automatically undertake the loan-related action inresponse to one of the plurality of entities moving from a firstjurisdiction to a second jurisdiction.

An example system may include wherein the loan-related action comprisesat least one loan-related action selected from the loan-related actionsconsisting of: offering the loan, accepting the loan, underwriting theloan, setting an interest rate for the loan, deferring a paymentrequirement, modifying an interest rate for the loan, validating titlefor collateral, recording a change in title, assessing a value ofcollateral, initiating inspection of collateral, calling the loan,closing the loan, setting terms and conditions for the loan, providingnotices required to be provided to a borrower, foreclosing on propertysubject to the loan, and modifying terms and conditions for the loan.

An example system may include wherein the smart contract circuit isfurther structured to process a plurality of jurisdiction-specificregulatory notice requirements and to provide an appropriate notice to aborrower based on a jurisdiction corresponding to at least one entityselected from the entities consisting of: a lender, a borrower, fundsprovided via the loan, a repayment of the loan, or a collateral for theloan.

An example system may include wherein the smart contract circuit isfurther structured to process a plurality of jurisdiction-specificregulatory foreclosure requirements and to provide an appropriateforeclosure notice to a borrower based on a jurisdiction correspondingto at least one entity selected from the entities consisting of: alender, a borrower, funds provided via the loan, a repayment of theloan, or a collateral for the loan.

An example system may include wherein the smart contract circuit isfurther structured to process a plurality of jurisdiction-specific rulesfor setting terms and conditions of the loan and to configure a smartcontract based on a jurisdiction corresponding to at least one entityselected from the entities consisting of: a borrower, funds provided viathe loan, a repayment of the loan, and a collateral for the loan.

An example system may include wherein the smart contract circuit isfurther structured to determine an interest rate for the loan to causethe loan to comply with a maximum interest rate limitation applicable ina jurisdiction corresponding to a selected one of the plurality ofentities.

An example system may include wherein the data collection circuit isfurther structured to monitor a condition of a collateral for the loan,and wherein the smart contract circuit is further structured todetermine the interest rate for the loan in response to the condition ofthe collateral for the loan.

An example system may include wherein the data collection circuit isfurther structured to monitor an attribute of at least one of theplurality of entities that are party to the loan, and wherein the smartcontract circuit is further structured to determine the interest ratefor the loan in response to the attribute.

An example system may include wherein the smart contract circuit furthercomprises a loan management circuit for specifying terms and conditionsof smart contracts that govern at least one of loan terms andconditions, loan-related events, or loan-related activities.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring management, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein a terms and conditions for theloan each comprise at least one member selected from the groupconsisting of: a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

An example system may include wherein the data collection circuitfurther comprises at least one system selected from the systemsconsisting of: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

An example system may include a valuation circuit is structured to use avaluation model to determine a value for a collateral for the loan basedon the jurisdiction corresponding to at least one of the plurality ofentities.

An example system may include wherein the valuation model is ajurisdiction-specific valuation model, and wherein the jurisdictioncorresponding to at least one of the plurality of entities comprises ajurisdiction corresponding to at least one entity selected from theentities consisting of: a lender, a borrower, funds provided pursuant tothe loan, a delivery location of funds provided pursuant to the loan, apayment of the loan, and a collateral for the loan.

An example system may include wherein at least one of the terms andconditions for the loan is based on the value of the collateral for theloan.

An example system may include wherein the collateral comprises at leastone item selected from the items consisting of: a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

An example system may include wherein the valuation circuit furthercomprises a transactions outcome processing circuit structured tointerpret outcome data relating to a transaction in collateral anditeratively improve the valuation model in response to the outcome data.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report on marketplace information relevant to the value of thecollateral.

An example system may include wherein the market value data collectioncircuit monitors pricing data or financial data for an offset collateralitem in at least one public marketplace.

An example system may include wherein the clustering circuit constructsa set of offset collateral items for valuing an item of collateral basedon an attribute of the collateral.

An example system may include wherein the attribute is selected fromamong: a category of the collateral, an age of the collateral, acondition of the collateral, a history of the collateral, a storagecondition of the collateral, and a geolocation of the collateral.

In embodiments, provided herein is a smart contract method for modifyinga loan having a set of computational services. An example method mayinclude monitoring location information corresponding to each one of aplurality of entities involved in a loan; determining a jurisdiction forat least one of the plurality of entities in response to the locationinformation; and automatically undertaking a loan-related action for theloan based at least in part on the jurisdiction for at least one of theplurality of entities.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may include automatically undertaking the loan-relatedaction in response to a first one of the plurality of entities being ina first jurisdiction, and a second one of the plurality of entitiesbeing in a second jurisdiction.

An example method may include automatically undertaking the loan-relatedaction in response to one of the plurality of entities moving from afirst jurisdiction to a second jurisdiction.

An example method may include processing a plurality ofjurisdiction-specific requirements based on a jurisdiction of a relevantone of the plurality of entities, and performing at least one operationselected from the operations consisting of: providing an appropriatenotice to a borrower in response to the plurality ofjurisdiction-specific requirements comprising regulatory noticerequirements; setting specific rules for setting terms and conditions ofthe loan in response to the plurality of jurisdiction-specificrequirements comprising jurisdiction-specific rules for terms andconditions of the loan; determining an interest rate for the loan tocause the loan to comply with a maximum interest rate limitation inresponse to the plurality of jurisdiction-specific requirementscomprising a maximum interest rate limitation; and wherein the relevantone of the plurality of entities comprises at least one entity selectedfrom the entities consisting of: a lender, a borrower, funds providedpursuant to the loan, a repayment of the loan, and a collateral for theloan.

An example method may include monitoring at least one of a condition ofa plurality of collateral for the loan or an attribute of at least oneof the plurality of entities that are party to the loan, wherein thecondition or the attribute is used to determine an interest rate.

An example method may include operating a valuation model to determine avalue for a collateral for the loan based on the jurisdiction for atleast one of the plurality of entities.

An example method may include interpreting outcome data relating to atransaction in collateral and iteratively improving the valuation modelin response to the outcome data.

In embodiments, provided herein is a smart contract system for modifyinga loan. An example platform, system, or apparatus may include a datacollection circuit structured to monitor and collect information aboutat least one entity involved in a loan; and a smart contract circuitstructured to automatically restructure a debt related to the loan basedon the monitored and collected information about the at least one entityinvolved in the loan.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the monitored and collectedinformation comprises a condition of a collateral for the loan.

An example system may include wherein the smart contract circuit may befurther structured to determine the occurrence of an event based on acovenant of the loan and the monitored and collected information aboutthe at least one entity involved in the loan, and to automaticallyrestructure the debt in response to the occurrence of the event.

An example system may include wherein the event is a failure ofcollateral for the loan to exceed a required fractional value of aremaining balance of the loan.

An example system may include wherein the event is a default of a buyerwith respect to the covenant.

An example system may include wherein the monitored and collectedinformation comprises an attribute of the at least one entity involvedin the loan.

An example system may include wherein the smart contract circuit furthercomprises a loan management circuit structured to specify terms andconditions of a smart contract that governs at least one of loan termsand conditions, a loan-related event or a loan-related activity.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein a terms and conditions for theloan each comprise at least one member selected from the groupconsisting of: a principal amount of debt, a balance of debt, a fixedinterest rate, a variable interest rate, a payment amount, a paymentschedule, a balloon payment schedule, a specification of collateral, aspecification of substitutability of collateral, a party, a guarantee, aguarantor, a security, a personal guarantee, a lien, a duration, acovenant, a foreclose condition, a default condition, and a consequenceof default.

An example system may include wherein the data collection circuitfurther comprises at least one system selected from the systemsconsisting of: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

An example system may further include a valuation circuit structured touse a valuation model to determine a value for a collateral based onmonitored and collected information about the at least one entityinvolved in the loan.

An example system may include wherein the restructuring of the debt isbased on a valuation of the collateral for the loan that is monitored bythe data collection circuit.

An example system may include wherein the collateral comprises at leastone item selected from the items consisting of: a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

An example system may include wherein the valuation circuit furthercomprises a transactions outcome processing circuit structured tointerpret outcome data relating to a transaction in collateral and toiteratively improve the valuation model in response to the outcome data.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report on marketplace information relevant to a value of collateral.

An example system may include wherein the market value data collectioncircuit monitors pricing or financial data for an offset collateral itemin at least one public marketplace.

An example system may include wherein a set of offset collateral itemsfor valuing an item of collateral is constructed using a clusteringcircuit based on an attribute of the collateral.

An example system may include wherein the attribute is selected fromamong a category of the collateral, an age of the collateral, acondition of the collateral, a history of the collateral, a storagecondition of the collateral, and a geolocation of the collateral.

In embodiments, provided herein is a smart contract method for modifyinga loan. An example method may include monitoring and collectinginformation about at least one entity involved in a loan; andautomatically restructuring a debt related to the loan based on themonitored and collected information about the at least one entity.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments.

An example method may include determining the occurrence of an eventbased on a covenant of the loan and the monitored and collectedinformation about the at least one entity involved in the loan, andautomatically restructuring the debt in response to the occurrence ofthe event.

An example method may include specifying terms and conditions of a smartcontract that governs at least one of loan terms and conditions, aloan-related event, or a loan-related activity.

An example method may include operating a valuation model to determine avalue for a collateral based on the monitored and collected informationabout the at least one entity involved in the loan.

An example method may further include interpreting outcome data relatingto a transaction in collateral and iteratively improving the valuationmodel in response to the outcome data.

An example method may further include monitoring and reporting onmarketplace information relevant to the value for the collateral.

An example method may further include monitoring pricing or financialdata for an offset collateral item in at least one public marketplace.

An example method may further include constructing a set of offsetcollateral items for valuing the collateral using a similarityclustering algorithm based on an attribute of the collateral.

An apparatus may include a data collection circuit structured to monitorand collect information about at least one of a borrower or a collateralfor the loan; and a smart contract circuit structured to automaticallyrestructure a debt related to the loan based on the monitored andcollected information about the at least one of the borrower or thecollateral for the loan.

The data collection circuit may be structured to monitor and collectinformation about the collateral for the loan, and wherein the monitoredand collected information comprises a condition of the collateral forthe loan.

The apparatus may further include a valuation circuit structured to anduse a valuation model to determine a value for the collateral for theloan based at least in part on the condition of the collateral for theloan.

The valuation circuit may further include a transactions outcomeprocessing circuit structured to interpret outcome data relating to atransaction in collateral and iteratively improve the valuation model inresponse to the outcome data.

In embodiments, provided herein is a social network monitoring systemfor validating conditions of a guarantee for a loan. An exampleplatform, system, or apparatus may include a social networking inputcircuit structured to interpret a loan guarantee parameter; a socialnetwork data collection circuit structured to collect data using aplurality of algorithms that are configured to monitor social networkinformation about an entity involved in a loan in response to the loanguarantee parameter; and a guarantee validation circuit structured tovalidate a guarantee for the loan in response to the monitored socialnetwork information.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the loan guarantee parametercomprises a financial condition of the entity, wherein the entity is aguarantor for the loan.

An example system may include wherein the guarantee validation circuitis further structured to determine the financial condition based on atleast one attribute selected from the attributes consisting of: apublicly stated valuation of the entity, a property owned by the entityas indicated by public records, a valuation of a property owned by theentity, a bankruptcy condition of the entity, a foreclosure status ofthe entity, a contractual default status of the entity, a regulatoryviolation status of the entity, a criminal status of the entity, anexport controls status of the entity, an embargo status of the entity, atariff status of the entity, a tax status of the entity, a credit reportof the entity, a credit rating of the entity, a web site rating of theentity, a plurality of customer reviews for a product of the entity, asocial network rating of the entity, a plurality of credentials of theentity, a plurality of referrals of the entity, a plurality oftestimonials for the entity, a plurality of behaviors of the entity, alocation of the entity, a jurisdiction of the entity, and a geolocationof the entity.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include a data collection circuit structured toobtain information about a condition of a collateral for the loan,wherein the collateral comprises at least one item selected from theitems consisting of: a vehicle, a ship, a plane, a building, a home,real estate property, undeveloped land, a farm, a crop, a municipalfacility, a warehouse, a set of inventory, a commodity, a security, acurrency, a token of value, a ticket, a cryptocurrency, a consumableitem, an edible item, a beverage, a precious metal, an item of jewelry,a gemstone, an item of intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property; and wherein the guarantee validation circuit isfurther structured to validate the guarantee of the loan in response tothe condition of the collateral for the loan.

An example system may include wherein the condition of the collateralcomprises a condition attribute selected from the group consisting of: aquality of the collateral, a status of title to the collateral, a statusof possession of the collateral, a status of a lien on the collateral, anew or used status, a type, a category, a specification, a productfeature set, a model, a brand, a manufacturer, a status, a context, astate, a value, a storage location, a geolocation, an age, a maintenancehistory, a usage history, an accident history, a fault history, anownership, an ownership history, a price, an assessment, and avaluation.

An example system may include wherein the social networking inputcircuit is further structured to enable a workflow by which a human userenters the loan guarantee parameter to establish a social network datacollection and monitoring request.

An example system may include a smart contract circuit structured toautomatically undertake an action related to the loan in response to thevalidation of the loan.

An example system may include wherein the action related to the loan isin response to the loan guarantee not being validated, and wherein theaction comprises at least one action selected from the actionsconsisting of: a foreclosure action, a lien administration action, aninterest-rate adjustment action, a default initiation action, asubstitution of collateral, a calling of the loan, and providing analert to a second entity involved in the loan.

An example system may include a robotic process automation circuitstructured to, based on iteratively training on a training data setcomprising human user interactions with the social network datacollection circuit, configure the loan guarantee parameter based on atleast one attribute of the loan.

An example system may include wherein the at least one attribute of theloan is obtained from a smart contract circuit that manages the loan.

An example system may include wherein the training data set furthercomprises outcomes from a plurality of social network data collectionand monitoring requests performed by the social network data collectioncircuit.

An example system may include wherein the robotic process automationcircuit is further structured to determine at least one domain to whichthe social network data collection circuit will apply.

An example system may include wherein training comprises training therobotic process automation circuit to configure the plurality ofalgorithms.

In embodiments, provided herein is a social network monitoring methodfor validating conditions of a guarantee for a loan. An example methodmay include interpreting a loan guarantee parameter; collecting datausing a plurality of algorithms that are configured to monitor socialnetwork information about an entity involved in a loan in response tothe loan guarantee parameter; and validating a guarantee for the loan inresponse to the monitored social network information.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may further include enabling a workflow by which a humanuser enters the loan guarantee parameter to establish a social networkdata collection and monitoring request.

An example method may further include automatically undertaking anaction related to the loan in response to the validation of the loan.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a foreclosure action.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a lien administration action.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises an interest-rate adjustment action.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a default initiation action.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a substitution of collateral.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a calling of the loan.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises providing an alert to a second entityinvolved in the loan.

An example method may further include iteratively training a roboticprocess automation circuit to configure a data collection and monitoringaction based on at least one attribute of the loan, wherein the roboticprocess automation circuit is trained on a training data set comprisingat least one of outcomes from or human user interactions with theplurality of algorithms.

An example method may further include determining at least one domain towhich the plurality of algorithms will apply. For example, the algorithmmay query a plurality of domains in determining.

An example apparatus may include a social networking input circuitstructured to interpret a loan guarantee parameter; a social networkdata collection circuit structured to collect data using a plurality ofalgorithms that are configured to monitor social network informationabout a guarantor of the loan in response to the loan guaranteeparameter; and a guarantee validation circuit structured to validate aguarantee for the loan in response to the monitored social networkinformation.

The loan guarantee parameter may include a financial condition of theguarantor of the loan, and wherein the guarantee validation circuit isfurther structured to determine the financial condition of the guarantorof the loan based on at least one attribute selected from the attributesconsisting of: a publicly stated valuation of the entity, a set ofproperty owned by the entity as indicated by public records, a valuationof a set of property owned by the entity, a bankruptcy condition of theentity, a foreclosure status of the entity, a contractual default statusof the entity, a regulatory violation status of the entity, a criminalstatus of an entity, an export controls status of the entity, an embargostatus of the entity, a tariff status of the entity, a tax status of theentity, a credit report of the entity, a credit rating of the entity, aweb site rating of the entity, a set of customer reviews for a productof the entity, a social network rating of the entity, a set ofcredentials of the entity, a set of referrals of the entity, a set oftestimonials for the entity, a set of behavior of the entity, a locationof the entity, and a geolocation of the entity.

In embodiments, provided herein is a monitoring system for validatingconditions of a guarantee for a loan. An example platform, system, orapparatus may include an Internet of Things (IoT) data input circuitstructured to interpret a loan guarantee parameter; an IoT datacollection circuit structured to collect data using at least onealgorithm that is configured to monitor IoT information collected fromand about an entity involved in a loan in response to the loan guaranteeparameter; and a guarantee validation circuit structured to validate aguarantee for the loan in response to the monitored IoT information.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the loan guarantee parametercomprises a financial condition of the entity, wherein the entity is aguarantor for the loan.

An example system may include wherein the monitored IoT informationcomprises at least one of: a publicly stated valuation of the entity, aproperty owned by the entity as indicated by public records, a valuationof a property owned by the entity, a bankruptcy condition of the entity,a foreclosure status of the entity, a contractual default status of theentity, a regulatory violation status of the entity, a criminal statusof the entity, an export controls status of the entity, an embargostatus of the entity, a tariff status of the entity, a tax status of theentity, a credit report of the entity, a credit rating of the entity, awebsite rating of the entity, a plurality of customer reviews for aproduct of the entity, a social network rating of the entity, aplurality of credentials of the entity, a plurality of referrals of theentity, a plurality of testimonials for the entity, a plurality ofbehaviors of the entity, a location of the entity, a jurisdiction of theentity, and a geolocation of the entity.

An example system may include wherein the loan comprises at least oneloan type selected from the loan types consisting of: an auto loan, aninventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may include wherein the IoT data collection circuit isfurther structured to obtain information about a condition of acollateral for the loan, wherein the collateral comprises at least oneitem selected from the items consisting of a vehicle, a ship, a plane, abuilding, a home, a real estate property, an undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property; and wherein theguarantee validation circuit is further structured to validate theguarantee of the loan in response to the condition of the collateral forthe loan.

An example system may include wherein the condition of the collateralcomprises a condition attribute selected from the group consisting of aquality of the collateral, a status of title to the collateral, a statusof possession of the collateral, a status of a lien on the collateral, anew or used status, a type, a category, a specification, a productfeature set, a model, a brand, a manufacturer, a status, a context, astate, a value, a storage location, a geolocation, an age, a maintenancehistory, a usage history, an accident history, a fault history, anownership, an ownership history, a price, an assessment, and avaluation.

An example system may include wherein the IoT data collection inputcircuit is further structured to enable a workflow by which a human userenters the loan guarantee parameter to establish an Internet of Thingsdata collection request.

An example system may include a smart contract circuit structured toautomatically undertake an action related to the loan in response to thevalidation of the loan.

An example system may include wherein the action related to the loan isin response to the loan guarantee not being validated, and wherein theaction comprises at least one action selected from the actionsconsisting of: a foreclosure action, a lien administration action, aninterest-rate adjustment action, a default initiation action, asubstitution of collateral, a calling of the loan, and providing analert to second entity involved in the loan.

An example system may include a robotic process automation circuitstructured to, based on iteratively training on a training data setcomprising human user interactions with the IoT data collection circuit,configure the loan guarantee parameter based on at least one attributeof the loan.

An example system may include wherein the at least one attribute of theloan is obtained from a smart contract circuit that manages the loan.

An example system may include wherein the training data set furthercomprises outcomes from a plurality of IoT data collection andmonitoring requests performed by the IoT data collection circuit.

An example system may include wherein the robotic process automationcircuit is further structured to determine at least one domain to whichthe IoT data collection circuit will apply.

An example system may include wherein the training comprises trainingthe robotic process automation circuit to configure the at least onealgorithm.

In embodiments, provided herein is a monitoring method for validatingconditions of a guarantee for a loan. An example method may includeinterpreting a loan guarantee parameter; collecting data using aplurality of algorithms that are configured to monitor Internet ofThings (IoT) information collected from and about an entity involved ina loan in response to the loan guarantee parameter; and validating aguarantee for the loan in response to the monitored IoT information.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may further include configuring the loan guaranteeparameter to obtain information about a financial condition of theentity, wherein the entity is a guarantor for the loan.

An example method may further include configuring the at least onealgorithm to obtain information about a condition of a collateral forthe loan, wherein the collateral comprises at least one item selectedfrom the items consisting of a vehicle, a ship, a plane, a building, ahome, a real estate property, an undeveloped land, a farm, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, an item of intellectual property, anintellectual property right, a contractual right, an antique, a fixture,an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property; and validating theguarantee for the loan further in response to the condition of thecollateral for the loan.

An example method may further include enabling a workflow by which ahuman user enters the loan guarantee parameter to establish an IoT datacollection request.

An example method may further include automatically undertaking anaction related to the loan in response to the validation of the loan.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a foreclosure action.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a lien administration action.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises an interest-rate adjustment action.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a default initiation action.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a substitution of collateral.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises a calling of the loan.

An example method may further include wherein the action related to theloan is in response to the loan guarantee not being validated, andwherein the action comprises providing an alert to a second entityinvolved in the loan.

An example method may further include iteratively training a roboticprocess automation circuit to configure an IoT data collection andmonitoring action based on at least one attribute of the loan, whereinthe robotic process automation circuit is trained on a training data setcomprising at least one of outcomes from or human user interactions withthe plurality of algorithms.

An example method may further include determining at least one domain towhich the plurality of algorithms will apply.

An example method may further include wherein training comprisestraining the robotic process automation circuit to configure pluralityof algorithms.

An example method may further include wherein the training data setfurther comprises outcomes from a set of IoT data collection andmonitoring requests.

In embodiments, provided herein is a robotic process automation systemfor negotiating a loan. An example platform, system, or apparatus mayinclude a data collection circuit structured to collect a training setof interactions from at least one entity related to at least one loantransaction; an automated loan classification circuit trained on thetraining set of interactions to classify a at least one loan negotiationaction; and a robotic process automation circuit trained on a trainingset of a plurality of loan negotiation actions classified by theautomated loan classification circuit and a plurality of loantransaction outcomes to negotiate a terms and conditions of a new loanon behalf of a party to the new loan.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the data collection circuit furthercomprises at least one system selected from the systems consisting of:an Internet of Things system, a camera system, a networked monitoringsystem, an internet monitoring system, a mobile device system, awearable device system, a user interface system, and an interactivecrowdsourcing system.

An example system may include wherein the at least one entity is a partyto the at least one loan transaction.

An example system may include wherein the at least one entity isselected from the entities consisting of: a primary lender, a secondarylender, a lending syndicate, a corporate lender, a government lender, abank lender, a secured lender, bond issuer, a bond purchaser, anunsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, and an accountant.

An example system may include wherein the automated loan classificationcircuit comprises a system selected from the systems consisting of: amachine learning system, a model-based system, a rule-based system, adeep learning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

An example system may include wherein the robotic process automationcircuit is further trained on a plurality of interactions of partieswith a plurality of user interfaces involved in a plurality of lendingprocesses.

An example system may further include a smart contract circuitstructured to automatically configure a smart contract for the new loanbased on an outcome of the negotiation.

An example system may further include a distributed ledger associatedwith the new loan, wherein the distributed ledger is structured torecord at least one of an outcome and a negotiating event of thenegotiation.

An example system may include wherein the new loan comprises at leastone loan type selected from the loan types consisting of: an auto loan,an inventory loan, a capital equipment loan, a bond for performance, acapital improvement loan, a building loan, a loan backed by an accountreceivable, an invoice finance arrangement, a factoring arrangement, apay day loan, a refund anticipation loan, a student loan, a syndicatedloan, a title loan, a home loan, a venture debt loan, a loan ofintellectual property, a loan of a contractual claim, a working capitalloan, a small business loan, a farm loan, a municipal bond, and asubsidized loan.

An example system may further include a valuation circuit structured touse a valuation model to determine a value for a collateral for the newloan.

An example system may include wherein the collateral comprises at leastone item selected from the items consisting of: a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, an item of intellectualproperty, an intellectual property right, a contractual right, anantique, a fixture, an item of furniture, an item of equipment, a tool,an item of machinery, and an item of personal property.

An example system may include wherein the valuation circuit furthercomprises a market value data collection circuit structured to monitorand report on marketplace information relevant to a value of thecollateral.

An example system may include wherein the market value data collectioncircuit monitors pricing or financial data for an offset collateral itemin at least one public marketplace.

An example system may include wherein a set of offset collateral itemsfor valuing the collateral is constructed using a clustering circuitbased on an attribute of the collateral.

An example system may include wherein the attribute is selected fromamong a category of the collateral, an age of the collateral, acondition of the collateral, a history of the collateral, a storagecondition of the collateral, and a geolocation of the collateral.

An example system may include wherein the terms and conditions for thenew loan comprise at least one member selected from the group consistingof: a principal amount of debt, a balance of debt, a fixed interestrate, a variable interest rate, a payment amount, a payment schedule, aballoon payment schedule, a specification of collateral, a specificationof substitutability of collateral, a party, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, and a consequence of default.

In embodiments, provided herein is a robotic process automation methodfor negotiating a loan. An example method may include collecting atraining set of interactions from at least one entity related to atleast one loan transaction; training an automated loan classificationcircuit on the training set of interactions to classify a at least oneloan negotiation action; and training a robotic process automationcircuit on a training set of a plurality of loan negotiation actionsclassified by the automated loan classification circuit and a pluralityof loan transaction outcomes to negotiate a terms and conditions of anew loan on behalf of a party to the new loan.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may further include

An example method may further include training the robotic processautomation circuit on a plurality of interactions of parties with aplurality of user interfaces involved in a plurality of lendingprocesses.

An example method may further include configuring a smart contract forthe new loan based on an outcome of the negotiation.

An example method may further include recording at least one of anoutcome and a negotiating event of the negotiation in a distributedledger associated with the new loan.

An example method may further include determining a value for acollateral for the new loan using a valuation model.

An example method may further include monitoring and reporting onmarketplace information relevant to a value of the collateral.

An example method may further include constructing a set of offsetcollateral items for valuing the collateral using a similarityclustering algorithm based on an attribute of the collateral.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement.

An example apparatus or system may include a data collection circuitstructured to interpret interactions among entities corresponding to aplurality of entities related to at least one transaction of a first setof loans, wherein the at least one transaction involves a firstcollection action of a set of payments corresponding to the first set ofloans; an artificial intelligence circuit structured to classify thefirst collection action, wherein the artificial intelligence circuit istrained on the interactions corresponding to the first set of loans; anda robotic process automation circuit that is trained on the interactionsand a set of loan collection outcomes corresponding to the first set ofloans to implement a second loan collection action on behalf of a partyto a second loan.

Certain further aspects of an example system or apparatus are describedfollowing, any one or more of which may be present in certainembodiments.

An example apparatus or system may include wherein the second loancollection action is selected from actions consisting of: initiation ofa collection process, referral of a loan to an agent for collection,configuration of a collection communication, scheduling of a collectioncommunication, configuration of content for a collection communication,configuration of an offer to settle a loan, termination of a collectionaction, deferral of a collection action, configuration of an offer foran alternative payment schedule, initiation of a litigation, initiationof a foreclosure, initiation of a bankruptcy process, initiation of arepossession process, and placement of a lien on collateral.

An example apparatus or system may include wherein the set of loancollection outcomes is selected from outcomes consisting of: a responseto a collection contact event, a payment of a loan, a default of aborrower on a loan, a bankruptcy of a borrower of a loan, an outcome ofa collection litigation, a financial yield of a set of collectionactions, a return on investment on collection, and a measure ofreputation of a party involved in collection.

An example apparatus or system may include wherein the data collectioncircuit comprises at least one system selected from systems consistingof: an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

An example apparatus or system may include wherein the entities are aset of parties to a loan transaction.

An example apparatus or system may include wherein the set of parties isselected from parties consisting of: a primary lender, a secondarylender, a lending syndicate, a corporate lender, a government lender, abank lender, a secured lender, bond issuer, a bond purchaser, anunsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, and an accountant.

An example apparatus or system may include wherein the artificialintelligence circuit comprises at least one system selected from systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network a self-organizing map, a fuzzy logic system, arandom walk system, a random forest system, a probabilistic system, aBayesian system, and a simulation system.

An example apparatus or system may include wherein the robotic processautomation circuit is trained on a set of interactions of parties, thesystem further comprising at least one user interface configured tointeract with at least one party involved in a set of lending processes.

An example apparatus or system may include wherein upon completion ofnegotiation of a collection process a smart contract for a loan isautomatically configured by a smart contract circuit based on theoutcome of the negotiation.

An example apparatus or system may include wherein robotic processautomation circuit is structured to record the set of loan collectionoutcomes and the first collection action in a distributed ledgerassociated with the first set of loans.

An example apparatus or system may include wherein the second loancomprises at least one loan selected from a set of loans consisting of:auto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, anda subsidized loan.

An example apparatus or system may include wherein the artificialintelligence circuit includes at least one system from systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network, a self-organizing map, a fuzzy logic system,a random walk system, a random forest system, a probabilistic system, aBayesian system, and a simulation system.

An example apparatus or system may include wherein the entities eachcomprise at least one entity selected from the entities consisting of: alender, a borrower, a guarantor, equipment related to the first set ofloans, goods related to the first set of loans, a system related to thefirst set of loans, a fixture related to the first set of loans, abuilding, a storage facility, and an item of collateral.

An example apparatus or system may include wherein robotic processautomation circuit is structured to record the second loan collectionaction in a distributed ledger associated with the second loan.

An example apparatus or system may include wherein the first collectionaction is selected from the actions consisting of: an initiation of acollection process, a referral of a loan to an agent for collection, aconfiguration of a collection communication, a scheduling of acollection communication, a configuration of content for a collectioncommunication, a configuration of an offer to settle a loan, atermination of a collection action, a deferral of a collection action, aconfiguration of an offer for an alternative payment schedule, aninitiation of a litigation, an initiation of a foreclosure, aninitiation of a bankruptcy process, an initiation of a repossessionprocess, and a placement of a lien on collateral.

In embodiments, provided herein is a method for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example method may includeinterpreting a plurality of interactions among entities corresponding toa plurality of entities related to at least one transaction of a firstset of loans, wherein the at least one transaction involves a firstcollection action of a set of payments corresponding to the first set ofloans; classifying the first collection action based at least in part onthe plurality of interactions; and specifying, based at least in part onthe plurality of interactions and a set of loan collection outcomescorresponding to the first set of loans, a second loan collection actionon behalf of a party to a second loan.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample method may further include wherein the second loan collectionaction comprises at least one of initiation of a collection process,configuration of a collection communication, or scheduling of acollection action.

An example method may further include wherein the second loan collectionaction comprises at least one of referral of a loan to an agent forcollection, configuration of an offer to settle the second loan, orconfiguration of content for a collection communication.

An example method may further include wherein the second loan collectionaction comprises at least one of termination of a collection action,deferral of a collection action, or configuration of an offer for analternative payment schedule.

An example method may further include wherein the second loan collectionaction comprises at least one of initiation of a litigation, initiationof a foreclosure, or initiation of a bankruptcy process.

An example method may further include wherein the second loan collectionaction comprises at least one of initiation of a repossession process orplacement of a lien on collateral of the second loan.

An example method may further include wherein the set of loan collectionoutcomes is selected from outcomes consisting of: a response to acollection contact event, a payment of a loan, a default of a borroweron a loan, a bankruptcy of a borrower of a loan, an outcome of acollection litigation, a financial yield of a set of collection actions,a return on investment on collection, and a measure of reputation of aparty involved in collection.

An example method may further include wherein upon completion ofnegotiation of a collection process a smart contract for a loan isautomatically configured by a set of smart contract services based onthe outcome of the negotiation.

An example method may further include further comprising recording atleast one of the set of loan collection outcomes in a distributed ledgerassociated with the first set of loans.

An example method may further include further comprising providing auser interface to a party of the second loan, and notifying the party ofthe second loan of the specified second collection action.

An example method may further include further comprising initiating thespecified second collection action in response to an input from theparty of the second loan to the user interface.

An example method may further include further comprising recording thesecond loan collection action in a distributed ledger associated withthe second loan.

An example method may further include wherein the first loan collectionaction comprises at least one of initiation of a collection process,configuration of a collection communication, or scheduling of acollection action, referral of a loan to an agent for collection,configuration of an offer to settle the second loan, or configuration ofcontent for a collection communication.

An example method may further include wherein the first loan collectionaction comprises at least one of termination of a collection action,deferral of a collection action, or configuration of an offer for analternative payment schedule.

An example method may further include wherein the first loan collectionaction comprises at least one of initiation of a litigation, initiationof a foreclosure, or initiation of a bankruptcy process, initiation of arepossession process, or placement of a lien on collateral of the secondloan.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement.

An example apparatus or system may include a data collection circuitstructured to collect a training set of loan interactions betweenentities, wherein the training set of loan interactions comprises a setof loan refinancing activities and a set of loan refinancing outcomes;an artificial intelligence circuit structured to classify the set ofloan refinancing activities, wherein the artificial intelligence circuitis trained on the training set of loan interactions; and a roboticprocess automation circuit structured to perform a second loanrefinancing activity on behalf of a party to a second loan, wherein therobotic process automation circuit is trained on the set of loanrefinancing activities and the set of loan refinancing outcomes.

Certain further aspects of an example system or apparatus are describedfollowing, any one or more of which may be present in certainembodiments.

An example apparatus or system may include wherein at least one loanrefinancing activity of the set of loan refinancing activities isselected from a group consisting of: initiating an offer to refinance,initiating a request to refinance, configuring a refinancing interestrate, configuring a refinancing payment schedule, configuring arefinancing balance, configuring collateral for a refinancing, managinguse of proceeds of a refinancing, removing or placing a lien associatedwith a refinancing, verifying title for a refinancing, managing aninspection process, populating an application, negotiating terms andconditions for a refinancing, or closing a refinancing.

An example apparatus or system may include wherein the data collectioncircuit comprises at least one system selected from systems consistingof: Internet of Things systems that monitor the entities, a set ofcameras that monitor the entities, a set of software services that pullinformation related to the entities from publicly available informationsites, a set of mobile devices that report on information related to theentities, a set of wearable devices worn by human entities, a set ofuser interfaces by which entities provide information about the entitiesand a set of crowdsourcing services configured to solicit and reportinformation related to the entities.

An example apparatus or system may include wherein at least one entityof the entities is a party to at least one loan refinancing activity ofthe set of loan refinancing activities.

An example apparatus or system may include wherein the party is at leastone party selected from a group consisting of: a primary lender, asecondary lender, a lending syndicate, a corporate lender, a governmentlender, a bank lender, a secured lender, bond issuer, a bond purchaser,an unsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, or an accountant.

An example apparatus or system may include wherein the artificialintelligence circuit comprises at least one system selected from systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network, a self-organizing map, a fuzzy logic system,a random walk system, a random forest system, a probabilistic system, aBayesian system, or a simulation system.

An example apparatus or system may include further comprising aninterface circuit structured to receive interactions from at least oneof the entities and wherein the robotic process automation circuit isfurther trained on the interactions.

An example apparatus or system may include a smart contract circuitstructured to determine completion of the second loan refinancingactivity, and to modify a smart refinance contract based on an outcomeof the second loan refinancing activity.

An example apparatus or system may include a distributed ledger circuitstructured to determine an event associated with the second loanrefinancing activity, and to record, in a distributed ledger associatedwith the second loan, the event associated with the second loanrefinancing activity.

An example apparatus or system may include wherein the second loancomprises at least one loan selected from a group consisting of: an autoloan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a fam1 loan, a municipal bond, or asubsidized loan.

An example apparatus or system may include wherein the artificialintelligence circuit includes at least one system from systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network, a self-organizing map, a fuzzy logic system,a random walk system, a random forest system, a probabilistic system, aBayesian system, and a simulation system.

In embodiments, provided herein is a method for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example method may includecollecting a training set of loan interactions between entities, whereinthe training set of loan interactions comprises a set of loanrefinancing activities and a set of loan refinancing outcomes;classifying the set of loan refinancing activities based at least inpart on the training set of loan interactions; and specifying a secondloan refinancing activity on behalf of a party to a second loan based atleast in part on the set of loan refinancing activities and the set ofloan refinancing outcomes.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample method may further include

An example method may further include wherein at least one loanrefinancing activity of the set of loan refinancing activities includesinitiating an offer to refinance, initiating a request to refinance,configuring a refinancing interest rate, configuring a refinancingpayment schedule, configuring a refinancing balance, configuringcollateral for a refinancing, managing use of proceeds of a refinancing,removing or placing a lien associated with a refinancing, verifyingtitle for a refinancing, managing an inspection process, populating anapplication, negotiating terms and conditions for a refinancing, and thelike.

An example method may further include wherein at least one entity of theentities is a party to at least one loan refinancing activity of the setof loan refinancing activities. receiving interactions from at least oneof the entities, and wherein the classifying is further trained on theinteractions.

An example method may further include wherein the party is at least oneparty selected from a group consisting of: a primary lender, a secondarylender, a lending syndicate, a corporate lender, a government lender, abank lender, a secured lender, bond issuer, a bond purchaser, anunsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, or an accountant.

An example method may further include determining completion of thesecond loan refinancing activity; and modifying a smart refinancecontract based on an outcome of the second loan refinancing activity.

An example method may further include recording, in a distributed ledgerassociated with the second loan, one of the modified smart refinancecontract or a reference to the modified smart refinance contract.

An example method may further include determining an event associatedwith the second loan refinancing activity; and recording, in adistributed ledger associated with the second loan, the event associatedwith the second loan refinancing activity.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement.

An example apparatus or system may include a data collection circuitstructured to collect a training set of loan interactions betweenentities. The training set of loan interactions comprises a set of loanconsolidation transactions. The apparatus or system may further includean artificial intelligence circuit structured to classify a set of loansas candidates for consolidation, wherein the artificial intelligencecircuit is trained on training set of interactions; a robotic processautomation circuit structured to manage a consolidation of at least asubset of the set of loans on behalf of a party to the consolidation,wherein the robotic process automation circuit is trained on the set ofloan consolidation transactions.

Certain further aspects of an example system or apparatus are describedfollowing, any one or more of which may be present in certainembodiments.

An example apparatus or system may include wherein the data collectioncircuit comprises at least one system selected from systems consistingof: Internet of Things systems that monitor the entities, a set ofcameras that monitor the entities, a set of software services that pullinformation related to the entities from publicly available informationsites, a set of mobile devices that report on information related to theentities, a set of wearable devices worn by human entities, a set ofuser interfaces by which entities provide information about the entitiesand a set of crowdsourcing services configured to solicit and reportinformation related to the entities.

An example apparatus or system may include wherein the set of loans thatare classified as candidates for consolidation are determined based on amodel that processes attributes of the entities; and wherein at leastone attribute selected from a group consisting of: identity of a party,interest rate, payment balance, payment terms, payment schedule, type ofloan, type of collateral, financial condition of party, payment status,condition of collateral, or value of collateral.

An example apparatus or system may include wherein at least one managingthe consolidation includes managing selected from a group consisting of:identification of loans from a set of candidate loans, preparation of aconsolidation offer, preparation of a consolidation plan, preparation ofcontent communicating a consolidation offer, scheduling a consolidationoffer, communicating a consolidation offer, negotiating a modificationof a consolidation offer, preparing a consolidation agreement, executinga consolidation agreement, modifying collateral for a set of loans,handling an application workflow for consolidation, managing aninspection, managing an assessment, setting an interest rate, deferringa payment requirement, setting a payment schedule, or closing aconsolidation agreement.

An example apparatus or system may include wherein at least one entityof the entities is a party to at least one loan consolidationtransaction of the set of loan consolidation transactions.

An example apparatus or system may include wherein the party is at leastone party selected from a group consisting of: a primary lender, asecondary lender, a lending syndicate, a corporate lender, a governmentlender, a bank lender, a secured lender, bond issuer, a bond purchaser,an unsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, or an accountant.

An example apparatus or system may include wherein the artificialintelligence circuit comprises at least one system selected from systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network, a self-organizing map, a fuzzy logic system,a random walk system, a random forest system, a probabilistic system, aBayesian system, or a simulation system.

An example apparatus or system may further include an interface circuitstructured to receive interactions from at least one of the entities andwherein the robotic process automation circuit is further trained on theinteractions.

An example apparatus or system may further include a smart contractcircuit structured to determine completion of a negotiation of theconsolidation of at least one loan from the subset of the set of loans;and modify a smart consolidation contract based on an outcome of thenegotiation.

An example apparatus or system may further include a distributed ledgercircuit structured to determine at least one of an outcome and anegotiation event associated with the consolidation of at least thesubset of the set of loans; and record, in a distributed ledgerassociated with the subset of the set of loans, at least one of theoutcome and the negotiation event associated with the consolidation.

An example apparatus or system may include wherein at least one loanfrom the subset of the set of loans is selected from a group consistingof: an auto loan, an inventory loan, a capital equipment loan, a bondfor performance, a capital improvement loan, a building loan, a loanbacked by an account receivable, an invoice finance arrangement, afactoring arrangement, a pay day loan, a refund anticipation loan, astudent loan, a syndicated loan, a title loan, a home loan, a venturedebt loan, a loan of intellectual property, a loan of a contractualclaim, a working capital loan, a small business loan, a fam1 loan, amunicipal bond, or a subsidized loan.

In embodiments, provided herein is a method for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example method may includecollecting a training set of loan interactions between entities, whereinthe training set of loan interactions comprises a set of loanconsolidation transactions; classifying a set of loans as candidates forconsolidation based at least in part on the training set of loaninteractions; and managing a consolidation of at least a subset of theset of loans on behalf of a party to the consolidation based at least inpart on the set of loan consolidation transactions.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample method may further include classifying the set of loans ascandidates for consolidation is based on a model that processesattributes of the entities; and wherein at least one attribute selectedfrom a group consisting of: identity of a party, interest rate, paymentbalance, payment terms, payment schedule, type of loan, type ofcollateral, financial condition of party, payment status, condition ofcollateral, or value of collateral.

An example method may further include at least one entity of theentities is a party to at least one loan consolidation transaction ofthe set of loan consolidation transactions.

An example method may further include that at least one managing theconsolidation includes managing selected from a group consisting of:identification of loans from a set of candidate loans, preparation of aconsolidation offer, preparation of a consolidation plan, preparation ofcontent communicating a consolidation offer, scheduling a consolidationoffer, communicating a consolidation offer, negotiating a modificationof a consolidation offer, preparing a consolidation agreement, executinga consolidation agreement, modifying collateral for a set of loans,handling an application workflow for consolidation, managing aninspection, managing an assessment, setting an interest rate, deferringa payment requirement, setting a payment schedule, or closing aconsolidation agreement.

An example method may further include that at least one entity of theentities is a party to at least one loan consolidation transaction ofthe set of loan consolidation transactions.

An example method may further include that the party is at least oneparty selected from a group consisting of: a primary lender, a secondarylender, a lending syndicate, a corporate lender, a government lender, abank lender, a secured lender, bond issuer, a bond purchaser, anunsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, or an accountant.

An example method may further include determining completion of anegotiation of the consolidation of at least one loan from the subset ofthe set of loans; and modifying a smart consolidation contract based onan outcome of the negotiation.

An example method may further include determining at least one of anoutcome and a negotiation event associated with the consolidation of atleast the subset of the set of loans; and recording, in a distributedledger associated with the subset of the set of loans, at least one ofthe outcome and the negotiation event associated with the consolidation.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement.

An example apparatus or system may include a data collection circuitstructured to collect information about entities involved in a set offactoring loans and a training set of interactions between entities fora set of factoring loan transactions. The apparatus or system mayfurther include an artificial intelligence circuit structured toclassify the entities involved in the set of factoring loans, whereinthe artificial intelligence circuit is trained on the training set ofinteractions; and a robotic process automation circuit structured tomanage a factoring loan, wherein the robotic process automation circuitis trained on the set of factoring loan interactions.

Certain further aspects of an example system or apparatus are describedfollowing, any one or more of which may be present in certainembodiments.

An example apparatus or system may include wherein the data collectioncircuit comprises at least one system selected from systems consistingof: Internet of Things systems that monitor the entities, a set ofcameras that monitor the entities, a set of software services that pullinformation related to the entities from publicly available informationsites, a set of mobile devices that report on information related to theentities, a set of wearable devices worn by human entities, a set ofuser interfaces by which entities provide information about the entitiesand a set of crowdsourcing services configured to solicit and reportinformation related to the entities.

An example apparatus or system may include wherein the artificialintelligence circuit is further structured to use a model that processesattributes of entities involved in the set of factoring loans; andwherein at least one attribute selected from a group consisting of:assets used for factoring, identity of a party, interest rate, paymentbalance, payment terms, payment schedule, type of loan, type ofcollateral, financial condition of party, payment status, condition ofcollateral, or value of collateral.

An example apparatus or system may include wherein at least one managingthe factoring loan includes managing selected from a group consistingof: managing at least one of a set of assets for factoring,identification of loans for factoring from a set of candidate loans,preparation of a factoring offer, preparation of a factoring plan,preparation of content comn1tmicating a factoring offer, scheduling afactoring offer, communicating a factoring offer, negotiating amodification of a factoring offer, preparing a factoring agreement,executing a factoring agreement, modifying collateral for a set offactoring loans, handing transfer of a set of accounts receivable,handling an application workflow for factoring, managing an inspection,managing an assessment of a set of assets to be factored, setting aninterest rate, deferring a payment requirement, setting a paymentschedule, or dosing a factoring agreement.

An example apparatus or system may include wherein the assets used forfactoring include a set of accounts receivable.

An example apparatus or system may include wherein at least one managingthe factoring loan includes managing selected from a group consistingof: managing at least one of a set of assets for factoring,identification of loans for factoring from a set of candidate loans,preparation of a factoring offer, preparation of a factoring plan,preparation of content comn1tmicating a factoring offer, scheduling afactoring offer, communicating a factoring offer, negotiating amodification of a factoring offer, preparing a factoring agreement,executing a factoring agreement, modifying collateral for a set offactoring loans, handing transfer of a set of accounts receivable,handling an application workflow for factoring, managing an inspection,managing an assessment of a set of assets to be factored, setting aninterest rate, deferring a payment requirement, setting a paymentschedule, or dosing a factoring agreement.

An example apparatus or system may include wherein at least one entityof the entities is a party to at least one factoring loan transactionsof the set of factoring loan transactions.

An example apparatus or system may include wherein the party is at leastone party selected from parties consisting of: a primary lender, asecondary lender, a lending syndicate, a corporate lender, a governmentlender, a bank lender, a secured lender, bond issuer, a bond purchaser,an unsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, and an accountant.

An example apparatus or system may include wherein the artificialintelligence circuit comprises at least one system selected from systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network, a self-organizing map, a fuzzy logic system,a random walk system, a random forest system, a probabilistic system, aBayesian system, or a simulation system.

An example apparatus or system may further include interface circuitstructured to receive interactions from at least one of the entities andwherein the robotic process automation circuit is further trained on theinteractions.

An example apparatus or system may further include a smart contractcircuit structured to determine completion of a negotiation of thefactoring loan; and modify a smart factoring loan contract based on anoutcome of the negotiation.

An example apparatus or system may further include a distributed ledgercircuit structured to determine at least one of an outcome and anegotiation event associated with the negotiation of the factoring loan;and record, in a distributed ledger associated with the factoring loan,at least one of the outcome and the negotiation event associated withthe factoring loan.

In embodiments, provided herein is a method for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example method may includecollecting information about entities involved in a set of factoringloans and a training set of interactions between entities for a set offactoring loan transactions; classifying the entities involved in theset of factoring loans based at least in part on the training set ofinteractions; and managing a factoring loan based at least in part onthe set of factoring loan interactions.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample method may further include that at least one managing thefactoring loan includes managing selected from a group consisting of:managing at least one of a set of assets for factoring, identificationof loans for factoring from a set of candidate loans, preparation of afactoring offer, preparation of a factoring plan, preparation of contentcomn1tmicating a factoring offer, scheduling a factoring offer,communicating a factoring offer, negotiating a modification of afactoring offer, preparing a factoring agreement, executing a factoringagreement, modifying collateral for a set of factoring loans, handingtransfer of a set of accounts receivable, handling an applicationworkflow for factoring, managing an inspection, managing an assessmentof a set of assets to be factored, setting an interest rate, deferring apayment requirement, setting a payment schedule, or dosing a factoringagreement.

An example method may further include that at least one entity of theentities is a party to at least one factoring loan transactions of theset of factoring loan transactions.

An example method may include that the party is at least one partyselected from a group consisting of: a primary lender, a secondarylender, a lending syndicate, a corporate lender, a government lender, abank lender, a secured lender, bond issuer, a bond purchaser, anunsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, or an accountant.

An example method may further include determining completion of anegotiation of the factoring loan; and modifying a smart factoring loancontract based on an outcome of the negotiation.

An example method may further include determining at least one of anoutcome and a negotiation event associated with the negotiation of thefactoring loan; and recording, in a distributed ledger associated withthe factoring loan, at least one of the outcome and the negotiationevent associated with the factoring loan.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement.

An example apparatus or system may include a data collection circuitstructured to collect information about entities involved in a set ofmortgage loan activities and a training set of interactions betweenentities for a set of mortgage loan transactions. The apparatus orsystem may further include an artificial intelligence circuit structuredto classify the entities involved in the set of mortgage loanactivities, wherein the artificial intelligence circuit is trained onthe training set of interactions; and a robotic process automationcircuit is structured broker a mortgage loan, wherein the roboticprocess automation circuit is trained on at least one of the set ofmortgage loan activities and the training set of interactions.

Certain further aspects of an example system or apparatus are describedfollowing, any one or more of which may be present in certainembodiments. An example apparatus or system may include wherein at leastone of the set of mortgage loan activities and the set of mortgage loantransactions includes activities selected from a group consisting of:among marketing activity, identification of a set of prospectiveborrowers, identification of property, identification of collateral,qualification of borrower, title search, title verification, propertyassessment, property inspection, property valuation, incomeverification, borrower demographic analysis, identification of capitalproviders, determination of available interest rates, determination ofavailable payment terms and conditions, analysis of existing mortgage,comparative analysis of existing and new mortgage terms, completion ofapplication workflow, population of fields of application, preparationof mortgage agreement, completion of schedule to mortgage agreement,negotiation of mortgage terms and conditions with capital provider,negotiation of mortgage terms and conditions with borrower, transfer oftitle, placement of lien, or closing of mortgage agreement.

An example apparatus or system may include wherein the data collectioncircuit comprises at least one system selected from systems consistingof: Internet of Things systems that monitor the entities, a set ofcameras that monitor the entities, a set of software services that pullinformation related to the entities from publicly available informationsites, a set of mobile devices that report on information related to theentities, a set of wearable devices worn by human entities, a set ofuser interfaces by which entities provide information about the entitiesand a set of crowdsourcing services configured to solicit and reportinformation related to the entities.

An example apparatus or system may include wherein the artificialintelligence circuit is further structured to use a model that processesattributes of entities involved in the set of mortgage loan activities;and wherein at least one attribute selected from a group consisting of:properties that are subject to mortgages, assets used for collateral,identity of a party, interest rate, payment balance, payment terms,payment schedule, type of mortgage, type of property, financialcondition of party, payment status, condition of property, or value ofproperty.

An example apparatus or system may include wherein brokering themortgage loan comprises at least one activity selected from a groupconsisting of: managing at least one of a property that is subject to amortgage, identification of candidate mortgages from a set of borrowersituations, preparation of a mortgage offer, preparation of contentcommunicating a mortgage offer, scheduling a mortgage offer,communicating a mortgage offer, negotiating a modification of a mortgageoffer, preparing a mortgage agreement, executing a mortgage agreement,modifying collateral for a set of mortgage loans, handing transfer of alien, handling an application workflow, managing an inspection, managingan assessment of a set of assets to be subject to a mortgage, setting aninterest rate, deferring a payment requirement, setting a paymentschedule, or closing a mortgage agreement.

An example apparatus or system may include wherein at least one entityof the entities is a party to at least one mortgage loan transactions ofthe set of mortgage loan transactions.

An example apparatus or system may include wherein the party is at leastone party selected from parties consisting of: a primary lender, asecondary lender, a lending syndicate, a corporate lender, a governmentlender, a bank lender, a secured lender, bond issuer, a bond purchaser,an unsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, and an accountant.

An example apparatus or system may include wherein the artificialintelligence circuit comprises at least one system selected from systemsconsisting of: a machine learning system, a model-based system, arule-based system, a deep learning system, a hybrid system, a neuralnetwork, a convolutional neural network, a feed forward neural network,a feedback neural network, a self-organizing map, a fuzzy logic system,a random walk system, a random forest system, a probabilistic system, aBayesian system, or a simulation system.

An example apparatus or system may further include an interface circuitstructured to receive interactions from at least one of the entities andwherein the robotic process automation circuit is further trained on theinteractions.

An example apparatus or system may further include a smart contractcircuit structured to determine completion of a negotiation of themortgage loan; and modify a smart factoring loan contract based on anoutcome of the negotiation.

An example apparatus or system may further include a distributed ledgercircuit structured to determine at least one of an outcome and anegotiation event associated with the negotiation of the mortgage loan;and record, in a distributed ledger associated with the mortgage loan,at least one of the outcome and the negotiation event associated withthe mortgage loan.

In embodiments, provided herein is a method for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example method may includecollecting information about entities involved in a set of mortgage loanactivities and a training set of interactions between entities for a setof mortgage loan transactions; classifying the entities involved in theset of mortgage loan activities based at least in part on the trainingset of interactions; and brokering a mortgage loan based at least inpart on at least one of the set of mortgage loan activities and thetraining set of interactions.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample method may further include classifying the entities involved inthe set of mortgage loan activities is based on a model that processesattributes of entities involved in the set of mortgage loan activities;and wherein at least one attribute selected from a group consisting of:properties that are subject to mortgages, assets used for collateral,identity of a party, interest rate, payment balance, payment terms,payment schedule, type of mortgage, type of property, financialcondition of party, payment status, condition of property, or value ofproperty.

An example method may further include that at least one brokering themortgage loan includes an activity selected from a group consisting of:managing at least one of a property that is subject to a mortgage,identification of candidate mortgages from a set of borrower situations,preparation of a mortgage offer, preparation of content communicating amortgage offer, scheduling a mortgage offer, communicating a mortgageoffer, negotiating a modification of a mortgage offer, preparing amortgage agreement, executing a mortgage agreement, modifying collateralfor a set of mortgage loans, handing transfer of a lien, handling anapplication workflow, managing an inspection, managing an assessment ofa set of assets to be subject to a mortgage, setting an interest rate,deferring a payment requirement, setting a payment schedule, or closinga mortgage agreement.

An example method may include that the at least one entity of theentities is a party to at least one mortgage loan transactions of theset of mortgage loan transactions.

An example method may include that the party is at least one partyselected from a group consisting of: a primary lender, a secondarylender, a lending syndicate, a corporate lender, a government lender, abank lender, a secured lender, bond issuer, a bond purchaser, anunsecured lender, a guarantor, a provider of security, a borrower, adebtor, an underwriter, an inspector, an assessor, an auditor, avaluation professional, a government official, or an accountant.

An example method may further include determining completion of anegotiation of the mortgage loan; and modifying a smart factoring loancontract based on an outcome of the negotiation.

An example method may further include determining at least one of anoutcome and a negotiation event associated with the negotiation of themortgage loan; and recording, in a distributed ledger associated withthe mortgage loan, at least one of the outcome and the negotiation eventassociated with the mortgage loan.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement.

An example system may include a data collection circuit structured tocollect information about entities involved in a set of debttransactions, training data set of outcomes related to the entities, anda training set of debt management activities. The system may furtherinclude a condition classifying circuit structured to classify acondition of at least one entity of the entities, wherein the conditionclassifying circuit comprises a model and a set of artificialintelligence circuits, and wherein the model is trained using thetraining data set of outcomes related to the entities; and an automateddebt management circuit structured to manage an action related to adebt, wherein the automated debt management circuit is trained on thetraining set of debt management activities.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the data collection circuit comprisesat least one system selected from a group consisting of: Internet ofThings devices, a set of environmental condition sensors, a set ofcrowdsourcing services, a set of social network analytic services, or aset of algorithms for querying network domains.

An example system may include wherein at least one debt transaction ofthe set of debt transactions is selected from a group consisting of: anauto loan, an inventory loan, a capital equipment loan, a bond forperformance, a capital improvement loan, a building loan, a loan backedby an account receivable, an invoice finance arrangement, a factoringarrangement, a pay day loan, a refund anticipation loan, a student loan,a syndicated loan, a title loan, a home loan, a venture debt loan, aloan of intellectual property, a loan of a contractual claim, a workingcapital loan, a small business loan, a farm loan, a municipal bond, or asubsidized loan.

An example system may include wherein the entities involved in the setof debt transactions include at least one of set of parties and a set ofassets.

An example system may include wherein at least one asset from the set ofassets includes an asset selected from a group consisting of: municipalasset, a vehicle, a ship, a plane, a building, a home, real estateproperty, undeveloped land, a farm, a crop, a municipal facility, awarehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone,intellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, or an item of personalproperty.

An example system may further include a set of sensors positioned on atleast one asset from the set of assets, on a container for least oneasset from the set of assets, and on a package for at least one assetfrom the set of assets, wherein the set of sensors configured toassociate sensor information sensed by the set of sensors with a uniqueidentifier for the at least one asset from the set of assets; and a setof block chain circuits structured to receive information from the datacollection circuit and the set of sensors and storing the information ina blockchain, wherein access to the blockchain is provided via a secureaccess control interface circuit for a party for a debt transactioninvolving the at least one asset from the set of assets.

An example system may include wherein at least one sensor from the setof sensors is selected from a group consisting of: image, temperature,pressure, humidity, velocity, acceleration, rotational, torque, weight,chemical, magnetic field, electrical field, or position sensors.

An example system may include an automated agent circuit structured toprocess events relevant to at least one of a value, a condition, and anownership of at least one asset of the set of assets and furtherstructured to undertake a set of actions related to a debt transactionto which the asset is related.

An example system may further include wherein at least one action of theset of actions is selected from a group consisting of: offering a debttransaction, underwriting a debt transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing thevalue of an asset, calling a loan, closing a transaction, setting tem1sand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating debt, orconsolidating debt.

An example system may further include wherein at least one artificialintelligence circuit from the set of artificial intelligence circuitsincludes at least one system selected from a group consisting of: amachine learning system, a model-based system, a rule-based system, adeep learning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, or a simulation system.

An example system may further include an interface circuit structured toreceive interactions from at least one of the entities and wherein theautomated debt management circuit is further trained on theinteractions.

An example system may further include wherein at least one debtmanagement activity from the training set of debt management activitiesincludes activities selected from a group consisting of: offering a debttransaction, underwriting a debt transaction, setting an interest rate,deferring a payment requirement, modifying an interest rate, validatingtitle, managing inspection, recording a change in title, assessing avalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating debt, orconsolidating debt.

An example system may further include a market value data collectioncircuit structured to monitor and report marketplace informationrelevant to a value of a of at least one asset of a set of assets.

An example system may further include wherein at least one asset fromthe set of assets is selected from group consisting of: a municipalasset, a vehicle, a ship, a plane, a building, a home, real estateproperty, undeveloped land, a farm, a crop, a municipal facility, awarehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone,intellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, or an item of personalproperty.

An example system may further include wherein the market value datacollection circuit is further structured to monitor at least one pricingand financial data for items that are similar to at least one asset inthe set of assets in at least one public marketplace.

An example system may further include wherein a set of similar items forvaluing at least one asset from the set of assets is constructed using asimilarity clustering algorithm based on attributes of the assets.

An example system may further include wherein at least one attribute ofthe attributes of the assets is selected from a group consisting of: acategory of assets, asset age, asset condition, asset history, assetstorage, or geolocation of assets.

An example system may further include a smart contract circuitstructured to manage a smart contract for a debt transaction.

An example system may further include wherein the smart contract circuitis further structured to establish a set of terms and conditions for thedebt transaction.

An example system may further include wherein at least one of the termsand conditions of the set of terms and conditions for the debttransaction is selected from a group consisting of: a principal amountof debt, a balance of debt, a fixed interest rate, a variable interestrate, a payment amount, a payment schedule, a balloon payment schedule,a specification of collateral, a specification of substitutability ofcollateral, a party, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, or a consequence of default.

In embodiments, provided herein is a method for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example method may includecollecting information about entities involved in a set of debttransactions, training data set of outcomes related to the entities, anda training set of debt management activities; classifying a condition ofat least one entity of the entities based at least in part the trainingdata set of outcomes related to the entities; and managing an actionrelated to a debt based at least in part on the training set of debtmanagement activities.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample method may further include that the entities involved in the setof debt transactions include a set of parties and a set of assets.

An example method may further include receiving information from a setof sensors positioned on at least one asset, wherein the set of sensorsconfigured to associate sensor information sensed by the set of sensorswith a unique identifier for the at least one asset from the set ofassets, and wherein the set of sensors is positioned on at least oneasset from the set of assets, on a container for least one asset fromthe set of assets, and on a package for at least one asset from the setof assets; and storing the information in a blockchain, wherein accessto the blockchain is provided via a secure access control interface fora party for a debt transaction involving the at least one asset from theset of assets.

An example method may include processing events relevant to at least oneof a value, a condition, and an ownership of at least one asset of theset of assets; and processing a set of actions related to a debttransaction to which the asset is related.

An example method may include receiving interactions from at least oneof the entities.

An example method may further include monitoring and reportingmarketplace information relevant to a value of a of at least one assetof a set of assets.

An example method may further include that monitoring further comprisesmonitoring at least one pricing and financial data for items that aresimilar to at least one asset in the set of assets in at least onepublic marketplace.

An example method may further include constructing using a similarityclustering algorithm based on attributes of the assets a set of similaritems for valuing at least one asset from the set of assets.

An example method may further include managing a smart contract for adebt transaction.

An example method may further include establishing a set of terms andconditions for the smart contract for the debt transaction.

In embodiments, provided herein is a system for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement.

An example system may include a crowdsourcing data collection circuitstructured to collect information about entities involved in a set ofbond transactions and a training data set of outcomes related to theentities. The system may further include a condition classifying circuitstructured to classify a condition of a set of issuers using theinformation from the crowdsourcing data collection circuit and a model,wherein the model is trained using the training data set of outcomesrelated to the set of issuers.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein at least one entity from the entitiesis selected from a group consisting of: a set of entities includesentities among a set of issuers, a set of bonds, a set of parties, or aset of assets.

An example system may include wherein at least one issuer from the setof issuers is selected from a group consisting of: a municipality, acorporation, a contractor, a government entity, a non-governmentalentity, or a non-profit entity.

An example system may include wherein at least one bond from the set ofbonds is selected from a group consisting of: a municipal bond, agovernment bond, a treasury bond, an asset-backed bond, or a corporatebond.

An example system may include wherein the condition classified by thecondition classifying circuit is selected from a group consisting of: adefault condition, a foreclosure condition, a condition indicatingviolation of a covenant, a financial risk condition, a behavioral riskcondition, a policy risk condition, a financial health condition, aphysical defect condition, a physical health condition, an entity riskcondition, or an entity health condition.

An example system may include wherein the crowdsourcing data collectioncircuit is structured to enable a user interface by which a user mayconfigure a crowdsourcing request for information relevant to thecondition about the set of issuers.

An example system may further include a configurable data collection andmonitoring circuit structured to monitor at least one issuer from theset of issuers, wherein the configurable data collection and monitoringcircuit includes a system selected from a group consisting of: Internetof Things devices, a set of environmental condition sensors, a set ofsocial network analytic services, or a set of algorithms for queryingnetwork domains.

An example system may include wherein the configurable data collectionand monitoring circuit is structured to monitor an at least oneenvironment selected from the group consisting of: a municipalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, or a vehicle.

An example system may include wherein a set of bonds associated with theset of bond transactions is backed by a set of assets.

An example system may include wherein at least one asset from the set ofassets includes assets selected from the group consisting of: municipalasset, a vehicle, a ship, a plane, a building, a home, real estateproperty, undeveloped land, a farm, a crop, a municipal facility, awarehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone,intellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, or an item of personalproperty.

An example system may include an automated agent circuit structured toprocesses events relevant to at least one of a value, a condition, andan ownership of at least one asset of the set of assets, and wherein theautomated agent circuit is further structured to perform an actionrelated to a debt transaction to which the asset is related.

An example system may include wherein the action is selected from agroup consisting of: offering a debt transaction, underwriting a debttransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating title, managing inspection,recording a change in title, assessing the value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating debt, or consolidating debt.

An example system may include wherein the condition classifying circuitincludes a system selected from a group consisting of: a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, or a simulation system.

An example system may further include an automated bond managementcircuit configured to manage an action related to the bond, wherein theautomated bond management circuit is trained on a training set of bondmanagement activities.

An example system may include wherein the automated bond managementcircuit is trained on a set of interactions of parties with a set ofuser interfaces involved in a set of bond transaction activities.

An example system may include wherein at least one bond transaction fromthe set of bond transaction includes activities selected from a groupconsisting of: a debt transaction, underwriting a debt transaction,setting an interest rate, deferring a payment requirement, modifying aninterest rate, validating title, managing inspection, recording a changein title, assessing the value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating debt, or consolidating debt.

An example system may further include a market value data collectioncircuit structured to monitor and reports on marketplace informationrelevant to a value of at least one of the issuer and a set of assets.

An example system may include wherein reporting is on a at least oneasset from the set of assets selected from a group consisting of: amunicipal asset, a vehicle, a ship, a plane, a building, a home, realestate property, undeveloped land, a farm, a crop, a municipal facility,a warehouse, a set of inventory, a commodity, a security, a currency, atoken of value, a ticket, a cryptocurrency, a consumable item, an edibleitem, a beverage, a precious metal, an item of jewelry, a gemstone,intellectual property, an intellectual property right, a contractualright, an antique, a fixture, an item of furniture, an item ofequipment, a tool, an item of machinery, or an item of personalproperty.

An example system may include wherein the market value data collectioncircuit is structured to monitor pricing or financial data for itemsthat are similar to the assets in at least one public marketplace.

An example system may include wherein a set of similar items for valuingthe assets is constructed using a similarity clustering algorithm basedon attributes of the assets.

An example system may include wherein at least one attribute from theattributes is selected from a group consisting of: a category of theassets, asset age, asset condition, asset history, asset storage, orgeolocation of assets.

An example system may further include a smart contract circuitstructured for managing a smart contract for a bond transaction.

An example system may include wherein the smart contract circuit isstructured to determine terms and conditions for the bond.

An example system may include wherein at least one term and conditionfrom the set of terms and conditions for the debt transaction that isspecified and managed by the set of smart contract circuits is selectedfrom a group consisting of: a principal amount of debt, a balance ofdebt, a fixed interest rate, a variable interest rate, a payment amount,a payment schedule, a balloon payment schedule, a specification ofassets that back the bond, a specification of substitutability ofassets, a party, an issuer, a purchaser, a guarantee, a guarantor, asecurity, a personal guarantee, a lien, a duration, a covenant, aforeclose condition, a default condition, or a consequence of default.

In embodiments, provided herein is a method for adaptive intelligenceand robotic process automation capabilities of a transactional,financial and marketplace enablement. An example method may includecollecting information about entities involved in a set of bondtransactions of a set of bonds and a training data set of outcomesrelated to the entities; classifying a condition of a set of issuersusing the collected information and a model, wherein the model istrained using the training data set of outcomes related to the set ofissuers.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample method may further include processing events relevant to atleast one of a value, a condition, and an ownership of at least oneasset of the set of assets; and performing an action related to a debttransaction to which the asset is related.

An example method may further include managing an action related to thebond based at least in part a training set of bond managementactivities.

An example method may further include monitoring and reporting onmarketplace information relevant to a value of at least one of theissuer and a set of assets.

An example method may further include managing a smart contract for abond transaction.

An example method may further include determining terms and conditionsfor the smart contract for at least one bond.

In embodiments, provided herein is a system for monitoring a conditionof an issuer for a bond. An example platform, system, or apparatus mayinclude a social network data collection circuit structured to collectinformation about at least one entity involved in at least onetransaction comprising at least one bond; a condition classifyingcircuit structured to classify a condition of the at least one entity inaccordance with a model and based on information from the social networkdata collection circuit, wherein the model is trained using a trainingdata set of a plurality of outcomes related to the at least one entity;and an automated bond management circuit structured to manage an actionrelated to the at least one bond in response to the classified conditionof the at least one entity.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the at least one entity is selectedfrom the entities consisting of: a bond issuer, a bond, a party, and anasset.

An example system may include wherein the at least one entity comprisesa bond issuer selected from the bond issuers consisting of: amunicipality, a corporation, a contractor, a government entity, anon-governmental entity, and a non-profit entity.

An example system may include wherein the bond is selected from theentities consisting of: a municipal bond, a government bond, a treasurybond, an asset-backed bond, and a corporate bond.

An example system may include wherein the condition classified by thecondition classifying circuit comprises at least one condition selectedfrom the conditions consisting of: a default condition, a foreclosurecondition, a condition indicating violation of a covenant, a financialrisk condition, a behavioral risk condition, a policy risk condition, afinancial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition or an entity healthcondition.

An example system may include wherein the social network data collectioncircuit further comprises a social networking input circuit structuredto receive input from a user used to configure a query for informationabout the at least one entity in response to the received input.

An example system may further include a data collection circuitstructured to monitor at least one of an Internet of Things device, anenvironmental condition sensor, a crowdsourcing request circuit, acrowdsourcing communication circuit, a crowdsourcing publishing circuit,and an algorithm for querying network domains.

An example system may further include wherein the condition classifyingcircuit is further structured to classify the condition in response tothe information from the data collection circuit.

An example system may include wherein the data collection circuit isfurther structured to monitor an environment selected from the groupconsisting of: a municipal environment, a corporate environment, asecurities trading environment, a real property environment, acommercial facility, a warehousing facility, a transportationenvironment, a manufacturing environment, a storage environment, a home,and a vehicle.

An example system may further include wherein the condition classifyingcircuit is further structured to classify the condition in response tothe monitored environment.

An example system may include wherein the at least one bond is backed byat least one asset.

An example system may include wherein the at least one asset is selectedfrom the assets consisting of: a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property.

An example system may further include an event processing circuitstructured to process an event relevant to at least one of a value, acondition and an ownership of the at least one asset and to undertake anaction related to the at least one transaction in response to the event.

An example system may include wherein the action is selected from theactions consisting of: a bond transaction, underwriting a bondtransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating title, managing inspection,recording a change in title, assessing the value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating bonds, and consolidating bonds.

An example system may include wherein the condition classifying circuitcomprises a system selected from the systems consisting of: a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

An example system may further include an automated bond managementcircuit structured to manage an action related to the at least one bond,wherein the automated bond management circuit is trained on a trainingdata set of a plurality of bond management activities.

An example system may include wherein the automated bond managementcircuit is trained on a plurality of interactions of parties with aplurality of user interfaces involved in a plurality of bond transactionactivities.

An example system may include wherein the plurality of bond transactionactivities is selected from the bond transaction activities consistingof: offering a bond transaction, underwriting a bond transaction,setting an interest rate, deferring a payment requirement, modifying aninterest rate, validating title, managing inspection, recording a changein title, assessing a value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating bonds, and consolidating bonds.

An example system may further include a market value data collectioncircuit structured to monitor and report on marketplace informationrelevant to a value of at least one of a bond issuer, the at least onebond, and an asset related to the at least one bond.

An example system may include wherein the asset is selected from theassets consisting of: a municipal asset, a vehicle, a ship, a plane, abuilding, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property.

An example system may include wherein the market value data collectioncircuit is further structured to monitor pricing or financial data foran offset asset item in at least one public marketplace.

An example system may further include wherein comprising a clusteringcircuit structured to construct a set of offset asset items for valuingthe asset is constructed using a clustering circuit based on anattribute of the asset.

An example system may include wherein the attribute is selected from theattributes consisting of: a category, an asset age, an asset condition,an asset history, an asset storage, and a geolocation.

An example system may further include a smart contract circuitstructured to manage a smart contract for the at least one transaction.

An example system may include wherein the smart contract circuit isfurther structured to determine a terms and conditions for the at leastone bond.

An example system may include wherein the terms and conditions areselected from the group consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of assets that back the at least one bond, a specificationof substitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default. In embodiments, provided herein is a method formonitoring a condition of an issuer for a bond. An example method mayinclude collecting social network information about at least one entityinvolved in at least one transaction comprising at least one bond; andclassifying a condition of the at least one entity in accordance with amodel and based on the social network information, wherein the model istrained using a training data set of a plurality of outcomes related tothe at least one entity, and managing an action related to the at leastone bond in response to the classified condition of the at least oneentity.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may further include processing an event relevant to atleast one of a value, a condition, and an ownership of at least oneasset related to the at least one bond and undertaking an action relatedto the at least one transaction in response to the event. An examplemethod may further include training an automated bond management circuiton a training set of a plurality of bond management activities to managean action related to the at least one bond, and wherein managing theaction comprises operating the automated bond management circuit. Anexample method may further include monitoring and reporting onmarketplace information relevant to a value of at least one of a bondissuer, the at least one bond, and an asset.

In embodiments, provided herein is a system for monitoring a conditionof an issuer for a bond. An example platform, system, or apparatus mayinclude an Internet of Things data collection circuit structured tocollect information about at least one entity involved in at least onetransaction comprising at least one bond; and a condition classifyingcircuit structured to classify a condition of the at least one entity inaccordance with a model and based on information from the Internet ofThings data collection circuit, wherein the model is trained using atraining data set of a plurality of outcomes related to the at least oneentity, and an event processing circuit structured undertake an actionrelated to the at least one transaction in response to the classifiedcondition of the at least one entity.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the at least one entity is selectedfrom the entities consisting of: a bond issuer, a bond, a party, and anasset.

An example system may include wherein the bond issuer is selected fromthe bond issuers consisting of: a municipality, a corporation, acontractor, a government entity, a non-governmental entity, and anon-profit entity.

An example system may include wherein the bond is selected from theentities consisting of: a municipal bond, a government bond, a treasurybond, an asset-backed bond, and a corporate bond.

An example system may include wherein the condition classified by thecondition classifying circuit at least one of a default condition, aforeclosure condition, a condition indicating violation of a covenant, afinancial risk condition, a behavioral risk condition, a policy riskcondition, a financial health condition, a physical defect condition, aphysical health condition, an entity risk condition, or an entity healthcondition.

An example system may include wherein the Internet of Things datacollection circuit further comprises an Internet of Things input circuitstructured to receive input from a user used to configure a query forinformation about the at least one entity.

An example system may further include a data collection circuitstructured to monitor at least one of an Internet of Things device, anenvironmental condition sensor, a crowdsourcing request circuit, acrowdsourcing communication circuit, a crowdsourcing publishing circuit,and an algorithm for querying network domains.

An example system may further include wherein the condition classifyingcircuit is further structured to classify the condition in response tothe information from the data collection circuit.

An example system may include wherein the data collection circuit isfurther structured to monitor an environment selected from the groupconsisting of: a municipal environment, a corporate environment, asecurities trading environment, a real property environment, acommercial facility, a warehousing facility, a transportationenvironment, a manufacturing environment, a storage environment, a home,and a vehicle.

An example system may include wherein the condition classifying circuitis further structured to classify the condition in response to themonitored environment.

An example system may include wherein the at least one bond is backed byat least one asset.

An example system may include wherein the at least one asset is selectedfrom the assets consisting of: a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property.

An example system may further include an event processing circuitstructured to process an event relevant to at least one of a value, acondition, and an ownership of the at least one asset and undertake anaction related to the at least one transaction further in response tothe event.

An example system may include wherein the action is selected from theactions consisting of: a bond transaction, underwriting a bondtransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating title, managing inspection,recording a change in title, assessing the value of an asset, calling aloan, closing a transaction, setting tem1s and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating bonds, and consolidating bonds.

An example system may include wherein the condition classifying circuitcomprises a system selected from the systems consisting of: a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

An example system may further include an automated bond managementcircuit structured to manage an action related to the at least one bond,wherein the automated bond management circuit is trained on a trainingdata set of a plurality of bond management activities.

An example system may include wherein the automated bond managementcircuit is trained on a plurality of interactions of parties with aplurality of user interfaces involved in a plurality of bond transactionactivities.

An example system may include wherein the plurality of bond transactionactivities is selected from the bond transaction activities consistingof: offering a bond transaction, underwriting a bond transaction,setting an interest rate, deferring a payment requirement, modifying aninterest rate, validating title, managing inspection, recording a changein title, assessing a value of an asset, calling a loan, closing atransaction, setting terms and conditions for a transaction, providingnotices required to be provided, foreclosing on a set of assets,modifying terms and conditions, setting a rating for an entity,syndicating bonds, and consolidating bonds.

An example system may further include a market value data collectioncircuit structured to monitor and report on marketplace informationrelevant to a value of at least one of a bond issuer, the at least onebond, and an asset related to the at least one bond.

An example system may include wherein the asset is selected from theassets consisting of: a municipal asset, a vehicle, a ship, a plane, abuilding, a home, real estate property, undeveloped land, a farm, acrop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property.

An example system may include wherein the market value data collectioncircuit is further structured to monitor pricing or financial data foran offset asset item in at least one public marketplace.

An example system may further include a clustering circuit structured toconstruct wherein a set of offset asset items for valuing the asset isconstructed using a clustering circuit based on an attribute of theasset.

An example system may include wherein the attribute is selected from theattributes consisting of: a category, an asset age, an asset condition,an asset history, an asset storage, and a geolocation.

An example system may further include a smart contract circuitstructured to manage a smart contract for the at least one transaction.

An example system may include wherein the smart contract circuit isfurther structured to determine a terms and conditions for the at leastone bond.

An example system may include wherein the terms and conditions areselected from the group consisting of: a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of assets that back the at least one bond, a specificationof substitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, and aconsequence of default.

In embodiments, provided herein is a method for monitoring a conditionof an issuer for a bond. An example method may include collectingInternet of Things information about at least one entity involved in atleast one transaction comprising at least one bond; and classifying acondition of the at least one entity in accordance with a model andbased on the Internet of Things information, wherein the model istrained using a training data set of a plurality of outcomes related tothe at least one entity and undertaking an action related to the atleast one transaction in response to the classified condition of the atleast one entity.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may further include processing an event relevant to atleast one of a value, a condition, and an ownership of at least oneasset and undertaking an action related to the at least one transactionin response to the event. An example method may further include trainingan automated bond management circuit on a training set of a plurality ofbond management activities to manage an action related to the at leastone bond. An example method may further include monitoring and reportingon marketplace information relevant to a value of at least one of a bondissuer, the at least one bond, and an asset.

In embodiments, an example platform or system may include an Internet ofThings data collection circuit structured to collect information aboutat least one entity involved in at least one subsidized loantransaction; a condition classifying circuit comprising a modelstructured to classify at least one parameter of at least one subsidizedloan involved in the at least one subsidized loan transaction based onthe information from the Internet of Things data collection circuit,wherein the model is trained using a training data set of a plurality ofoutcomes related to the at least one subsidized loan: and a smartcontract circuit structured to automatically modify a terms andconditions of the at least one subsidized loan based on the classifiedparameter from the condition classifying circuit.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the at least one entity is selectedfrom the entities consisting of: the at least one subsidized loan, adistinct at least one subsidized loan involved in the at least onesubsidized loan transaction, a party, a subsidy, a guarantor, asubsidizing party, and a collateral.

An example system may include wherein the at least one entity comprisesa party is selected from the parties consisting of: at least one of amunicipality, a corporation, a contractor, a government entity, anon-governmental entity, and a non-profit entity.

An example system may include wherein the at least one subsidized loancomprises at least one of a municipal subsidized loan, a governmentsubsidized loan, a student loan, an asset-backed subsidized loan, or acorporate subsidized loan.

An example system may include wherein the condition classified by thecondition classifying circuit is selected from the conditions consistingof: a default condition, a foreclosure condition, a condition indicatingviolation of a covenant, a financial risk condition, a behavioral riskcondition, a contractual performance condition, a policy risk condition,a financial health condition, a physical defect condition, a physicalhealth condition, an entity risk condition and an entity healthcondition.

An example system may include wherein the at least one subsidized loanis a student loan and the condition classifying circuit classifies atleast one of a progress of a student toward a degree, a participation ofa student in a non-profit activity, and a participation of a student ina public interest activity.

An example system may include wherein further comprising a userinterface of the Internet of Things data collection circuit structuredto enable a user to configure a query for information about the at leastone entity.

An example system may include wherein further comprising at least oneconfigurable data collection and circuit structured to monitor the atleast one entity selected from the group consisting of: a social networkanalytic circuit, an environmental condition circuit, a crowdsourcingcircuit, and an algorithm for querying a network domain.

An example system may include wherein the at least one configurable datacollection and circuit monitors an environment selected from theenvironments consisting of: a municipal environment, an educationalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle.

An example system may include wherein the at least one subsidized loanis backed by at least one asset.

An example system may include wherein the at least one asset is selectedfrom the assets consisting of: a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property.

An example system may include wherein further comprising an automatedagent structured to process at least one event relevant to at least oneof a value, a condition, and an ownership of the at least one asset andundertake an action related to the at least one subsidized loantransaction to which the at least one asset is related.

An example system may include wherein the action is selected from theactions consisting of: a subsidized loan transaction, underwriting asubsidized loan transaction, setting an interest rate, deferring apayment requirement, modifying an interest rate, validating a title,managing an inspection, recording a change in a title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating a subsidizedloan, and consolidating a subsidized loan.

An example system may include wherein the condition classifying circuitcomprises a system selected from the systems consisting of: a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

An example system may include wherein further comprising an automatedsubsidized loan management circuit structured to manage an actionrelated to the at least one subsidized loan, wherein the automatedsubsidized loan management circuit is trained on a training set ofsubsidized loan management activities.

An example system may include wherein the automated subsidized loanmanagement circuit is trained on a plurality of interactions of partieswith a plurality of user interfaces involved in a plurality ofsubsidized loan transaction activities.

An example system may include wherein the plurality of subsidized loantransaction activities are selected from the activities consisting of:offering a subsidized loan transaction, underwriting a subsidized loantransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating a title, managing an inspection,recording a change in a title, assessing a value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating a subsidized loan, and consolidating a subsidizedloan.

An example system may include wherein further comprising a blockchainservice circuit structured to record the modified set of terms andconditions for the at least one subsidized loan in a distributed ledger.

An example system may include wherein further comprising a market valuedata collection circuit structured to monitor and report on marketplaceinformation relevant to a value of at least one of an issuer, at leastone subsidized loan, and at least one asset.

An example system may include wherein reporting is on at least one assetselected from the assets consisting of: a municipal asset, a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property.

An example system may include wherein the market value data collectioncircuit is further structured to monitor pricing or financial data foran offset asset item in at least one public marketplace.

An example system may include a clustering circuit structured toconstruct a set of offset asset items for valuing the at least one assetis constructed using a clustering circuit based on an attribute of theat least one asset.

An example system may include wherein the attribute is selected from theattributes consisting of a category, an asset age, an asset condition,an asset history, an asset storage, and a geolocation.

An example system may include wherein further comprising a smartcontract circuit structured to manage a smart contract for the at leastone subsidized loan transaction.

An example system may include wherein the smart contract is furtherstructured to modify the smart contract in response to the classifiedparameter of the at least one subsidized loan.

An example system may include wherein the terms and conditions for theat least one subsidized loan that are automatically modified by thesmart contract circuit are selected from the group consisting of: aprincipal amount of debt, a balance of debt, a fixed interest rate, avariable interest rate, a payment amount, a payment schedule, a balloonpayment schedule, a specification of assets that back the at least onesubsidized loan, a specification of substitutability of assets, a party,an issuer, a purchaser, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, and a consequence of default.

In embodiments, an example method may include collecting informationabout at least one entity involved in at least one subsidized loantransaction; classifying at least one parameter of at least onesubsidized loan involved in the at least one subsidized loan transactionbased on the information using a model trained on a training data set ofa plurality of outcomes related to the at least one subsidized loan; andautomatically modifying a terms and conditions of the at least onesubsidized loan based on the classified parameter.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may include wherein further comprising processing atleast one event relevant to at least one of a value, a condition, or anownership of the at least one asset related to the at least onesubsidized loan and undertaking an action related to the at least onesubsidized loan transaction to which the at least one asset is related.

An example method may include wherein further comprising recording themodified set of terms and conditions for the at least one subsidizedloan in a distributed ledger.

An example method may include wherein further comprising monitoring andreporting on marketplace information relevant to a value of at least oneof an issuer, the at least one subsidized loan, or at least one assetrelated to the at least one subsidized loan.

In embodiments, an example platform or system may include a socialnetwork analytic data collection circuit structured to collect socialnetwork information about at least one entity involved in at least onesubsidized loan transaction; a condition classifying circuit comprisinga model structured to classify at least one parameter of at least onesubsidized loan involved in the at least one subsidized loan transactionbased on the social network information from the social network analyticdata collection circuit, wherein the model is trained using a trainingdata set of outcomes related to the at least one subsidized loan; and asmart contract circuit structured to automatically modify a terms andconditions of the at least one subsidized loan based on the classifiedat least one parameter.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the at least one entity is selectedfrom the entities consisting of the at least one subsidized loan, adistinct at least one subsidized loan involved in the at least onesubsidized loan transaction, a party, a subsidy, a guarantor, asubsidizing party, and a collateral.

An example system may include wherein a party subsidizing the at leastone subsidized loan is selected from the parties consisting of: amunicipality, a corporation, a contractor, a government entity, anon-governmental entity, and a non-profit entity.

An example system may include wherein the at least one subsidized loancomprises at least one of a municipal subsidized loan, a governmentsubsidized loan, a student loan, an asset-backed subsidized loan, or acorporate subsidized loan.

An example system may include wherein the at least one parameterclassified by the condition classifying circuit is selected from theconditions consisting of: a default condition, a foreclosure condition,a condition indicating violation of a covenant, a financial riskcondition, a behavioral risk condition, a contractual performancecondition, a policy risk condition, a financial health condition, aphysical defect condition, a physical health condition, an entity riskcondition and an entity health condition.

An example system may include wherein the at least one subsidized loanis a student loan and the condition classifying circuit classifies atleast one of a progress of a student toward a degree, a participation ofa student in a non-profit activity, or a participation of a student in apublic interest activity.

An example system may include wherein further comprising a userinterface of the social network analytic data collection circuitstructured to enable a user to configure a query for information aboutthe at least one entity, wherein, in response to the query, wherein thesocial network analytic data collection circuit initiates at least onealgorithm that searches and retrieves data from at least one socialnetwork in response to the query.

An example system may include wherein further comprising at least oneconfigurable data collection and circuit structured to monitor the atleast one entity, and selected from the group consisting of: a socialnetwork analytic circuit, an environmental condition circuit, acrowdsourcing circuit, and an algorithm for querying a network domain.

An example system may include wherein the at least one configurable datacollection and circuit monitors an environment selected from theenvironments consisting of: a municipal environment, an educationalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle.

An example system may include wherein the at least one subsidized loanis backed by at least one asset.

An example system may include wherein the at least one asset is selectedfrom the assets consisting of: a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property.

An example system may include wherein further comprising an automatedagent structured to process at least one event relevant to at least oneof a value, a condition, or an ownership of the at least one asset andundertake an action related to the at least one subsidized loantransaction to which the at least one asset is related.

An example system may include wherein the action is selected from theactions consisting of: a subsidized loan transaction, underwriting asubsidized loan transaction, setting an interest rate, deferring apayment requirement, modifying an interest rate, validating a title,managing an inspection, recording a change in a title, assessing thevalue of an asset, calling a loan, closing a transaction, setting termsand conditions for a transaction, providing notices required to beprovided, foreclosing on a set of assets, modifying terms andconditions, setting a rating for an entity, syndicating a subsidizedloan, and consolidating a subsidized loan.

An example system may include wherein the condition classifying circuitcomprises a system selected from the systems consisting of: a machinelearning system, a model-based system, a rule-based system, a deeplearning system, a hybrid system, a neural network, a convolutionalneural network, a feed forward neural network, a feedback neuralnetwork, a self-organizing map, a fuzzy logic system, a random walksystem, a random forest system, a probabilistic system, a Bayesiansystem, and a simulation system.

An example system may include wherein further comprising an automatedsubsidized loan management circuit structured to manage an actionrelated to the at least one subsidized loan, and wherein the automatedsubsidized loan management circuit is trained on a training set ofsubsidized loan management activities.

An example system may include wherein the automated subsidized loanmanagement circuit is trained on a plurality of interactions of partieswith a plurality of user interfaces involved in a plurality ofsubsidized loan transaction activities.

An example system may include wherein the plurality of subsidized loantransaction activities are selected from the activities consisting of:offering a subsidized loan transaction, underwriting a subsidized loantransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating a title, managing an inspection,recording a change in a title, assessing a value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating a subsidized loan, and consolidating a subsidizedloan.

An example system may include wherein further comprising a blockchainservice circuit structured to record the modified set of terms andconditions for the at least one subsidized loan in a distributed ledger.

An example system may include wherein further comprising a market valuedata collection circuit structured to monitor and report on marketplaceinformation relevant to a value of at least one of an issuer, at leastone subsidized loan, or at least one asset.

An example system may include wherein reporting is on at least one assetselected from the assets consisting of: a municipal asset, a vehicle, aship, a plane, a building, a home, real estate property, undevelopedland, a farm, a crop, a municipal facility, a warehouse, a set ofinventory, a commodity, a security, a currency, a token of value, aticket, a cryptocurrency, a consumable item, an edible item, a beverage,a precious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, and an item of personal property.

An example system may include wherein the market value data collectioncircuit is further structured to monitor pricing or financial data foran offset asset item in at least one public marketplace.

An example system may further include a clustering circuit structured toconstruct a set of offset asset items for valuing the at least one assetis constructed using a clustering circuit based on an attribute of theat least one asset.

An example system may include wherein the attribute is selected from theattributes consisting of: a category, an asset age, an asset condition,an asset history, an asset storage, and a geolocation.

An example system may include wherein further comprising a smartcontract circuit structured to manage a smart contract for the at leastone subsidized loan transaction.

An example system may include wherein the smart contract circuit sets aterms and conditions for the at least one subsidized loan.

An example system may include wherein the terms and conditions for theat least one subsidized loan that are specified and managed by the smartcontract circuit are selected from the group consisting of: a principalamount of debt, a balance of debt, a fixed interest rate, a variableinterest rate, a payment amount, a payment schedule, a balloon paymentschedule, a specification of assets that back the at least onesubsidized loan, a specification of substitutability of assets, a party,an issuer, a purchaser, a guarantee, a guarantor, a security, a personalguarantee, a lien, a duration, a covenant, a foreclose condition, adefault condition, and a consequence of default.

In embodiments, an example method may include collecting social networkinformation about at least one entity involved in at least onesubsidized loan transaction; classifying at least one parameter of atleast one subsidized loan involved in the at least one subsidized loantransaction based on the social network information using a modeltrained on a training data set of outcomes related to the at least onesubsidized loan; automatically modifying a terms and conditions of theat least one subsidized loan based on the classified at least oneparameter.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may include wherein further comprising processing atleast one event relevant to at least one of a value, a condition, and anownership of the at least one asset and undertaking an action related tothe at least one subsidized loan transaction to which the at least oneasset is related.

An example method may include wherein further comprising recording themodified set of terms and conditions for the at least one subsidizedloan in a distributed ledger.

An example method may include wherein further comprising monitoring andreporting on marketplace information relevant to a value of at least oneof an issuer, the at least one subsidized loan, or at least one asset.

In embodiments, provided herein is a system for automating handling of asubsidized loan. An example platform or system may include acrowdsourcing services circuit structured to collect information relatedto a set of entities involved in a set of subsidized loan transactions;a condition classifying circuit comprising a model and an artificialintelligence services circuit structured to classify a set of parametersof the set of subsidized loans involved in the transactions based oninformation from the crowdsourcing services circuit, wherein the modelis trained using a training data set of outcomes related to subsidizedloans; and a smart contract circuit for automatically modifying theterms and conditions of a subsidized loan based on the classified set ofparameters from the condition classifying circuit.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the set of entities includes entitiesamong a set of subsidized loans, a set of parties, a set of subsidies, aset of guarantors, a set of subsidizing parties, and a set ofcollateral.

An example system may include wherein each entity of the set of entitiesincludes entities selected from the list consisting of: a subsidizedloan from a set of subsidized loans corresponding to the set ofsubsidized loan transactions, a party related to at least one of the setof subsidized loan transactions, a subsidy corresponding to a subsidizedloan from a set of subsidized loans corresponding to the set ofsubsidized loan transactions, a guarantor related to at least one of theset of subsidized loan transactions, a subsidy corresponding to asubsidized loan from a set of subsidized loans corresponding to the setof subsidized loan transactions, a subsidized party related to at leastone of the set of subsidized loan transactions, a subsidizing partyrelated to at least one of the set of subsidized loan transactions, asubsidy corresponding to a subsidized loan from a set of subsidizedloans corresponding to the set of subsidized loan transactions, and anitem of collateral related to at least one of the set of subsidized loantransactions, a subsidy corresponding to a subsidized loan from a set ofsubsidized loans corresponding to the set of subsidized loantransactions.

An example system may at least one entity of the set of entitiesincludes a subsidizing party related to at least one of the set ofsubsidized loan transactions, wherein the subsidizing party includes atleast one of a municipality, a corporation, a contractor, a governmententity, a non-governmental entity, or a non-profit entity.

An example system may include wherein each loan of a set of subsidizedloans corresponding to the set of loan transactions includes at leastone of a municipal subsidized loan, a government subsidized loan, astudent loan, an asset-backed subsidized loan, or a corporate subsidizedloan.

An example system may include wherein the condition classified by thecondition classifying circuit is among a default condition, aforeclosure condition, a condition indicating violation of a covenant, afinancial risk condition, a behavioral risk condition, a contractualperformance condition, a policy risk condition, a financial healthcondition, a physical defect condition, a physical health condition, anentity risk condition, and an entity health condition.

An example system may include wherein the subsidized loan is a studentloan and the condition classifying circuit classifies at least one ofthe progress of a student toward a degree, the participation of astudent in a non-profit activity, and a participation of the student ina public interest activity.

An example system may include wherein the crowdsourcing services circuitis further structured with a user interface by which a user mayconfigure a query for information about the set of entities and thecrowdsourcing services circuit automatically configures a crowdsourcingrequest based on the query.

An example system may include further comprising a configurable datacollection and monitoring services circuit for monitoring the entitieswherein the configurable data collection and monitoring services circuitincludes at least one of a set of: Internet of Things services, a set ofenvironmental condition sensors, a set of social network analyticservices, and a set of algorithms for querying network domains.

An example system may include wherein the configurable data collectionand monitoring services circuit is further structured to monitor anenvironment selected from among a municipal environment, an educationalenvironment, a corporate environment, a securities trading environment,a real property environment, a commercial facility, a warehousingfacility, a transportation environment, a manufacturing environment, astorage environment, a home, and a vehicle.

An example system may include wherein the set of subsidized loans isbacked by a set of assets.

An example system may include wherein the set of assets, each selectedfrom among: a municipal asset, a vehicle, a ship, a plane, a building, ahome, real estate property, undeveloped land, a fa1m, a crop, amunicipal facility, a warehouse, a set of inventory, a commodity, asecurity, a currency, a token of value, a ticket, a cryptocurrency, aconsumable item, an edible item, a beverage, a precious metal, an itemof jewelry, a gemstone, intellectual property, an intellectual propertyright, a contractual right, an antique, a fixture, an item of furniture,an item of equipment, a tool, an item of machinery, and an item ofpersonal property.

An example system may include further comprising an automated agentcircuit structured to process events relevant to at least one of avalue, a condition, or an ownership of at least one asset of the set ofassets and undertakes an action related to a subsidized loan transactionto which the at least one asset is related.

An example system may include wherein the action is selected from amongoffering a subsidized loan transaction, underwriting a subsidized loantransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating title, managing inspection,recording a change in title, assessing the value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction, providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating subsidized loans, or consolidating subsidized loans.

An example system may include wherein the artificial intelligenceservices circuit comprises at least one of a machine learning system, amodel-based system, a rule-based system, a deep learning system, ahybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizingmap, a fuzzy logic system, a random walk system, a random forest system,a probabilistic system, a Bayesian system, and a simulation system.

An example system may include further comprising an automated subsidizedloan management circuit structured to manage an action related to thesubsidized loan, wherein the automated subsidized loan managementcircuit is trained on a training set of subsidized loan managementactivities.

An example system may include wherein the automated subsidized loanmanagement circuit is further trained on a set of interactions ofparties with a set of user interfaces, wherein the parties are involvedin a set of subsidized loan transaction activities.

An example system may include wherein the set of subsidized loantransaction activities includes activities each selected from amongoffering a subsidized loan transaction, underwriting a subsidized loantransaction, setting an interest rate, deferring a payment requirement,modifying an interest rate, validating title, managing inspection,recording a change in title, assessing the value of an asset, calling aloan, closing a transaction, setting terms and conditions for atransaction providing notices required to be provided, foreclosing on aset of assets, modifying terms and conditions, setting a rating for anentity, syndicating subsidized loans, or consolidating subsidized loans.

An example system may include further comprising a blockchain servicescircuit structured to record the modified set of terms and conditionsfor a set of subsidized loans corresponding to the set of subsidizedloan transactions in a distributed ledger.

An example system may include further comprising a market value datacollection service circuit structured to monitor and report onmarketplace information relevant to the value of at least one of a partyrelated to the subsidized loan, a set of subsidized loans correspondingto the set of subsidized loan transactions, and a set of assets.

An example system may include wherein reporting is on a set of assetsthat includes at least one of a municipal asset, a vehicle, a ship, aplane, a building, a home, real estate property, undeveloped land, afarm, a crop, a municipal facility, a warehouse, a set of inventory, acommodity, a security, a currency, a token of value, a ticket, acryptocurrency, a consumable item, an edible item, a beverage, aprecious metal, an item of jewelry, a gemstone, intellectual property,an intellectual property right, a contractual right, an antique, afixture, an item of furniture, an item of equipment, a tool, an item ofmachinery, or an item of personal property.

An example system may include wherein the market value data collectionservice circuit is further structured to monitor pricing or financialdata for items that are similar to the assets of the set of assets in atleast one public marketplace.

An example system may include wherein a set of similar items for valuingthe assets of the set of assets is constructed using a similarityclustering algorithm based on the attributes of the assets.

An example system may include wherein the attributes are selected fromamong a category of the assets, asset age, asset condition, assethistory, asset storage, or geolocation of assets.

An example system may include further comprising a smart contractservices circuit for managing a smart contract for the subsidized loan.

An example system may include wherein the smart contract servicescircuit is further structured to set terms and conditions for thesubsidized loan.

An example system may include wherein the terms and conditions for thedebt transaction that are specified and managed by the smart contractservices circuit is selected from among a principal amount of debt, abalance of debt, a fixed interest rate, a variable interest rate, apayment amount, a payment schedule, a balloon payment schedule, aspecification of assets that back the subsidized loan, a specificationof substitutability of assets, a party, an issuer, a purchaser, aguarantee, a guarantor, a security, a personal guarantee, a lien, aduration, a covenant, a foreclose condition, a default condition, or aconsequence of default.

In embodiments, provided herein is a method for facilitating automatinghandling of a subsidized loan. An example method may include collectinginformation related to a set of entities involved in a set of subsidizedloan transactions; classifying a set of parameters of the set ofsubsidized loans involved in the subsidized loan transactions based onan artificial intelligence service, a model, and information from acrowdsourcing service, wherein the model is trained using a trainingdata set of outcomes related to subsidized loans; and modifying termsand conditions of a subsidized loan based on the classified set ofparameters.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may include wherein the set of entities includes entitiesselected from among a set of subsidized loans, a set of parties, a setof subsidies, a set of guarantors, a set of subsidizing parties, or aset of collateral.

An example method may include wherein the set of entities comprise a setof subsidizing parties, and wherein each party of the set of subsidizingparties includes at least one of a municipality, a corporation, acontractor, a government entity, a non-governmental entity, or anon-profit entity.

An example method may include wherein the set of subsidized loansincludes at least one of a municipal subsidized loan, a governmentsubsidized loan, a student loan, an asset-backed subsidized loan, and acorporate subsidized loan.

An example method may include wherein the subsidized loan is a studentloan wherein the classifying is based on at least one of the progress ofa student toward a degree, the participation of a student in anon-profit activity, and the participation of the student in a publicinterest activity.

In embodiments, an example platform or system may include an assetidentification service circuit structured to interpret a plurality ofassets corresponding to a financial entity configured to take custody ofthe plurality of assets; an identity management service circuitstructured to authenticate a plurality of identifiers corresponding toactionable entities entitled to take action with respect to theplurality of assets, wherein the plurality of identifiers comprises atleast one credential; a blockchain service circuit structured to store aplurality of asset control features in a blockchain structure, whereinthe blockchain structure comprises a distributed ledger configuration;and a financial management circuit structured to communicate theinterpreted plurality of assets and authenticated plurality ofidentifiers to the blockchain service circuit for storage in theblockchain structure as asset control features, and wherein theblockchain service circuit is further structured to record the assetcontrol features in the distributed ledger configuration as asset events

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the at least one credential comprisesan owner credential, an agent credential, a beneficiary credential, atrustee credential, or a custodian credential.

An example system may include wherein the asset events include eventsselected from among: transfer of title, death of an owner, disability ofan owner, bankruptcy of an owner, foreclosure, placement of a lien, useof assets as collateral, designation of a beneficiary, undertaking aloan against assets, providing a notice with respect to assets,inspection of assets, assessment of assets, reporting on assets fortaxation purposes, allocation of ownership of assets, disposal ofassets, sale of assets, purchase of assets, or designation of anownership status.

An example system may include a data collection circuit structured tomonitor at least one of the interpretation of the plurality of assets,the authentication of the plurality of identifiers, and the recording ofasset events.

An example system may include wherein the actionable entities eachinclude at least one of an owner, a beneficiary, an agent, a trustee, ora custodian.

An example system may include a smart contract circuit structured tomanage the custody of the plurality of assets, and wherein at least oneasset event related to the plurality of assets is managed by the smartcontract circuit based on a plurality of terms and conditions embodiedin a smart contract configuration and based on data collected by thedata collection service circuit.

An example system may include wherein the at least one asset eventrelated to the plurality of assets comprises at least one event selectedfrom among a transfer of title, death of an owner, disability of anowner, bankruptcy of an owner, foreclosure, placement of a lien, use ofassets as collateral, designation of a beneficiary, undertaking a loanagainst assets, providing a notice with respect to assets, inspection ofassets, assessment of assets, reporting on assets for taxation purposes,allocation of ownership of assets, disposal of assets, sale of assets,purchase of assets, and designation of an ownership status.

An example system may include wherein the data collection circuitfurther includes at least one system selected from the systemsconsisting of: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

An example system may include wherein each of the asset identificationservice circuit, identity management service circuit, blockchain servicecircuit, and the financial management circuit further comprise acorresponding application programming interface (API) componentstructured to facilitate communication among the circuits of the system.The corresponding API components of the circuits further include userinterfaces structured to interact with a plurality of users of thesystem.

An example system may include the blockchain service circuit furtherstructured to share and distribute the asset events with the pluralityof actionable entities.

In embodiments, an example method may include interpreting a pluralityof assets corresponding to a financial entity configured to take custodyof the plurality of assets; authenticating a plurality of identifierscorresponding to actionable entities entitled to take action withrespect to the plurality of assets, wherein the plurality of identifierscomprises at least one credential; storing a plurality of asset controlfeatures in a blockchain structure, wherein the blockchain structurecomprises a distributed ledger configuration; and communicating theinterpreted plurality of assets and authenticated plurality ofidentifiers for storage in the blockchain structure as asset controlfeatures, wherein the asset control features are recorded in thedistributed ledger configuration as asset events.

An example method may include wherein the at least one credentialcomprises an owner credential, an agent credential, a beneficiarycredential, a trustee credential, or a custodian credential.

An example method may include wherein the asset events each include atleast on event selected from among transfer of title, death of an owner,disability of an owner, bankruptcy of an owner, foreclosure, placementof a lien, use of assets as collateral, designation of a beneficiary,undertaking a loan against assets, providing a notice with respect toassets, inspection of assets, assessment of assets, reporting on assetsfor taxation purposes, allocation of ownership of assets, disposal ofassets, sale of assets, purchase of assets, or designation of anownership status.

An example method may include monitoring at least one of theinterpretation of the plurality of assets, the authentication of theplurality of identifiers, or the recording of asset events.

An example method may include wherein the actionable entities eachcomprise at least one of an owner, a beneficiary, an agent, a trustee,or a custodian.

An example method may include managing the custody of the plurality ofassets, wherein at least one asset event related to the plurality ofassets is based on a plurality of terms and conditions embodied in asmart contract configuration and based on data collected by the dataabout the plurality of assets.

An example method may include wherein each asset event related to theplurality of assets comprises at least one event selected from among atransfer of title, death of an owner, disability of an owner, bankruptcyof an owner, foreclosure, placement of a lien, use of assets ascollateral, designation of a beneficiary, undertaking a loan againstassets, providing a notice with respect to assets, inspection of assets,assessment of assets, reporting on assets for taxation purposes,allocation of ownership of assets, disposal of assets, sale of assets,purchase of assets, or designation of an ownership status.

An example method may include wherein the monitoring is executed by atleast one of an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, or aninteractive crowdsourcing system.

An example method may include comprising sharing and distributing theasset events with the plurality of actionable entities.

An example method may include wherein interpreting the plurality ofassets comprises identifying the plurality of assets for which afinancial entity is responsible for taking custody.

An example method may include wherein authenticating the plurality ofidentifiers comprises verifying the plurality of identifierscorresponding to actionable entities are entitled to take action withrespect to the plurality of assets.

An example method may include wherein the blockchain structure isprovided in conjunction with a block-chain marketplace.

An example method may include wherein the block-chain marketplaceutilizes an automated blockchain-based transaction application.

An example method may include comprising storing asset transaction datain the blockchain structure based on interactions between actionableentities.

An example method may include wherein the blockchain structure is adistributed blockchain structure across a plurality of asset nodes.

An example method may include wherein at least one of the plurality ofassets is a virtual asset tag and interpreting the plurality of assetscomprises identifying the virtual asset tag.

An example method may include wherein the storing of the plurality ofasset control features comprising storing virtual asset tag data.

An example method may include wherein the virtual asset tag data is atleast one of location data or tracking data.

An example method may include wherein an identifier corresponding to atleast one of the financial entity or actionable entities is stored asvirtual asset tag data.

In embodiments, provided herein is a system for facilitating foreclosureon collateral. An example platform or system may include a lendingagreement storage circuit structured to store a plurality of lendingagreement data comprising at least one lending agreement, wherein the atleast one lending agreement comprises a lending condition data, thelending condition data comprising a terms and condition data of the atleast one lending agreement related to a foreclosure condition on atleast one asset that provides a collateral condition related to acollateral asset for securing a repayment obligation of the at least onelending agreement; a data collection services circuit structured tomonitor the lending condition data and to detect a default conditionbased on a change to the lending condition data; and a smart contractservices circuit structured to, when the default condition is detectedby the data collection services circuit, interpret the default conditionand communicate a default condition indication that initiates aforeclosure procedure based on the collateral condition and the defaultcondition.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system may include wherein the smart contract services circuitis further structured to communicate the detected default conditionindication is communicated to at least one of a smart lock and a smartcontainer to lock the collateral asset.

An example system may include wherein the foreclosure procedureconfigures and initiates a listing of the collateral asset on a publicauction site.

An example system may include wherein the foreclosure procedureconfigures and delivers a set of transport instructions for thecollateral asset.

An example system may include wherein the foreclosure procedureconfigures a set of instructions for a drone to transport the collateralasset.

An example system may include wherein the foreclosure procedureconfigures a set of instructions for a robotic device to transport thecollateral asset.

An example system may include wherein the foreclosure procedureinitiates a process for automatically substituting a set of substitutecollateral.

An example system may include wherein the foreclosure procedureinitiates a collateral tracking procedure.

An example system may include wherein the foreclosure procedureinitiates a collateral valuation process.

An example system may include wherein the foreclosure procedureinitiates a message to a borrower initiating a negotiation regarding theforeclosure.

An example system may include wherein the negotiation is managed by arobotic process automation system trained on a training set offoreclosure negotiations.

An example system may include wherein the negotiation relates tomodification of at least one of interest rate, payment terms, andcollateral for the at least one lending agreement.

An example system may include wherein the data collection servicescircuit further comprises at least one system selected from the systemsconsisting of: an Internet of Things system, a camera system, anetworked monitoring system, an internet monitoring system, a mobiledevice system, a wearable device system, a user interface system, and aninteractive crowdsourcing system.

An example system may include wherein each of the lending agreementstorage circuit, data collection services circuit, and smart contractservices circuit further comprise a corresponding applicationprogramming interface (API) component structured to facilitatecommunication among the circuits of the system.

An example system may include wherein the corresponding API componentsof the circuits further comprise user interfaces structured to interactwith a plurality of users of the system.

In embodiments, provided herein is a method for facilitating foreclosureon collateral. An example method may include storing a plurality oflending agreement data comprising at least one lending agreement,wherein the at least one lending agreement comprises a lending conditiondata, the lending condition data comprising a terms and condition dataof the at least one lending agreement related to a foreclosure conditionon at least one asset that provides a collateral condition related to acollateral asset for securing a repayment obligation of the at least onelending agreement; monitoring the lending condition data and to detect adefault condition based on a change to the lending condition data;interpreting the default condition; and communicating a defaultcondition indication that initiates a foreclosure procedure based on thecollateral condition.

Certain further aspects of an example method are described following,any one or more of which may be present in certain embodiments. Anexample method may include wherein the detected default conditionindication is communicated to at least one of a smart lock and a smartcontainer to lock the collateral asset.

An example method may include wherein the foreclosure procedureconfigures and initiates a listing of the collateral asset on a publicauction site.

An example method may include wherein the foreclosure procedureconfigures and delivers a set of transport instructions for thecollateral asset.

An example method may include wherein the foreclosure procedureconfigures a set of instructions for a drone to transport the collateralasset.

An example method may include wherein the foreclosure procedureconfigures a set of instructions for a robotic device to transport thecollateral asset.

An example method may include wherein the foreclosure procedureinitiates a process for automatically substituting a set of substitutecollateral.

An example method may include wherein the foreclosure procedureinitiates a collateral tracking procedure.

An example method may include wherein the foreclosure procedureinitiates a collateral valuation process.

An example method may include wherein the foreclosure procedureinitiates a message to a borrower initiating a negotiation regarding theforeclosure.

An example method may include wherein the negotiation is managed by arobotic process automation system trained on a training set offoreclosure negotiations.

An example method may include wherein the negotiation relates tomodification of at least one of interest rate, payment terms, orcollateral for the at least one lending agreement.

An example method may include wherein the monitoring is provided by atleast one of an Internet of Things system, a camera system, a networkedmonitoring system, an internet monitoring system, a mobile devicesystem, a wearable device system, a user interface system, or aninteractive crowdsourcing system.

An example method may include wherein providing communications formonitoring, interpreting, and communicating are through an applicationprogramming interface (API).

An example method may include wherein providing a user interfaceincorporating the API to interact with a plurality of users.

Referring to FIG. 111, an adaptive intelligence system 11104 may includean artificial intelligence system 11148, a digital twin system 11120,and an adaptive device (or edge) intelligence system 11130. Theartificial intelligence system 11148 may define a machine learning model11102 for performing analytics, simulation, decision making, andprediction making related to data processing, data analysis, simulationcreation, and simulation analysis of one or more of the transactionentities. The machine learning model 11102 is an algorithm and/orstatistical model that performs specific tasks without using explicitinstructions, relying instead on patterns and inference. The machinelearning model 11102 builds one or more mathematical models based ontraining data to make predictions and/or decisions without beingexplicitly programmed to perform the specific tasks. The machinelearning model 11102 may receive inputs of sensor data as training data,including event data 11124 and state data 11172 related to one or moreof the transaction entities through data collection systems 11118 andmonitoring systems 11106 and connectivity facilities 11116. The eventdata 11124 and state data 11172 may be stored in a data storage system11110 The sensor data input to the machine learning model 11102 may beused to train the machine learning model 11102 to perform the analytics,simulation, decision making, and prediction making relating to the dataprocessing, data analysis, simulation creation, and simulation analysisof the one or more of the transaction entities. The machine learningmodel 11102 may also use input data from a user or users of theinformation technology system. The machine learning model 11102 mayinclude an artificial neural network, a decision tree, a support vectormachine, a Bayesian network, a genetic algorithm, any other suitableform of machine learning model, or a combination thereof. The machinelearning model 11102 may be configured to learn through supervisedlearning, unsupervised learning, reinforcement learning, self learning,feature learning, sparse dictionary learning, anomaly detection,association rules, a combination thereof, or any other suitablealgorithm for learning.

The artificial intelligence system 11148 may also define the digitaltwin system 11120 to create a digital replica of one or more of thetransaction entities. The digital replica of the one or more of thetransaction entities may use substantially real-time sensor data toprovide for substantially real-time virtual representation of thetransaction entity and provides for simulation of one or more possiblefuture states of the one or more transaction entities. The digitalreplica exists simultaneously with the one or more transaction entitiesbeing replicated. The digital replica provides one or more simulationsof both physical elements and properties of the one or more transactionentities being replicated and the dynamics thereof, in embodiments,throughout the lifestyle of the one or more transaction entities beingreplicated. The digital replica may provide a hypothetical simulation ofthe one or more transaction entities, for example during a design phasebefore the one or more transaction entities are constructed orfabricated, or during or after construction or fabrication of the one ormore transaction entities by allowing for hypothetical extrapolation ofsensor data to simulate a state of the one or more transaction entities,such as during high stress, after a period of time has passed duringwhich component wear may be an issue, during maximum throughputoperation, after one or more hypothetical or planned improvements havebeen made to the one or more transaction entities, or any other suitablehypothetical situation. In some embodiments, the machine learning model11102 may automatically predict hypothetical situations for simulationwith the digital replica, such as by predicting possible improvements tothe one or more transaction entities, predicting when one or morecomponents of the one or more transaction entities may fail, and/orsuggesting possible improvements to the one or more transactionentities, such as changes to timing settings, arrangement, components,or any other suitable change to the transaction entities. The digitalreplica allows for simulation of the one or more transaction entitiesduring both design and operation phases of the one or more transactionentities, as well as simulation of hypothetical operation conditions andconfigurations of the one or more transaction entities. The digitalreplica allows for invaluable analysis and simulation of the one or moretransaction entities, by facilitating observation and measurement ofnearly any type of metric, including temperature, wear, light,vibration, etc. not only in, on, and around each component of the one ormore transaction entities, but in some embodiments within the one ormore transaction entities. In some embodiments, the machine learningmodel 11102 may process the sensor data including the event data 11124and the state data 11172 to define simulation data for use by thedigital twin system 11120. The machine learning model 11102 may, forexample, receive state data 11172 and event data 11124 related to aparticular transaction entity of the plurality of transaction entitiesand perform a series of operations on the state data 11172 and the eventdata 11124 to format the state data 11172 and the event data 11124 intoa format suitable for use by the digital twin system 11120 in creationof a digital replica of the transaction entity. For example, one or moretransaction entities may include a robot configured to augment productson an adjacent assembly line. The machine learning model 11102 maycollect data from one or more sensors positioned on, near, in, and/oraround the robot. The machine learning model 11102 may performoperations on the sensor data to process the sensor data into simulationdata and output the simulation data to the digital twin system 11120.The digital twin system 11120 simulation may use the simulation data tocreate one or more digital replicas of the robot, the simulationincluding for example metrics including temperature, wear, speed,rotation, and vibration of the robot and components thereof. Thesimulation may be a substantially real-time simulation, allowing for ahuman user of the information technology to view the simulation of therobot, metrics related thereto, and metrics related to componentsthereof, in substantially real time. The simulation may be a predictiveor hypothetical situation, allowing for a human user of the informationtechnology to view a predictive or hypothetical simulation of the robot,metrics related thereto, and metrics related to components thereof.

In some embodiments, the machine learning model 11102 and the digitaltwin system 11120 may process sensor data and create a digital replicaof a set of transaction entities of the plurality of transactionentities to facilitate design, real-time simulation, predictivesimulation, and/or hypothetical simulation of a related group oftransaction entities. The digital replica of the set of transactionentities may use substantially real-time sensor data to provide forsubstantially real-time virtual representation of the set of transactionentities and provide for simulation of one or more possible futurestates of the set of transaction entities. The digital replica existssimultaneously with the set of transaction entities being replicated.The digital replica provides one or more simulations of both physicalelements and properties of the set of transaction entities beingreplicated and the dynamics thereof, in embodiments throughout thelifestyle of the set of transaction entities being replicated. The oneor more simulations may include a visual simulation, such as awire-frame virtual representation of the one or more transactionentities that may be viewable on a monitor, using an augmented reality(AR) apparatus, or using a virtual reality (VR) apparatus. The visualsimulation may be able to be manipulated by a human user of theinformation technology system, such as zooming or highlightingcomponents of the simulation and/or providing an exploded view of theone or more transaction entities. The digital replica may provide ahypothetical simulation of the set of transaction entities, for exampleduring a design phase before the one or more transaction entities areconstructed or fabricated, or during or after construction orfabrication of the one or more transaction entities by allowing forhypothetical extrapolation of sensor data to simulate a state of the setof transaction entities, such as during high stress, after a period oftime has passed during which component wear may be an issue, duringmaximum throughput operation, after one or more hypothetical or plannedimprovements have been made to the set of transaction entities, or anyother suitable hypothetical situation. In some embodiments, the machinelearning model 11102 may automatically predict hypothetical situationsfor simulation with the digital replica, such as by predicting possibleimprovements to the set of transaction entities, predicting when one ormore components of the set of transaction entities may fail, and/orsuggesting possible improvements to the set of transaction entities,such as changes to timing settings, arrangement, components, or anyother suitable change to the transaction entities. The digital replicaallows for simulation of the set of transaction entities during bothdesign and operation phases of the set of transaction entities, as wellas simulation of hypothetical operation conditions and configurations ofthe set of transaction entities. The digital replica allows forinvaluable analysis and simulation of the one or more transactionentities, by facilitating observation and measurement of nearly any typeof metric, including temperature, wear, light, vibration, etc. not onlyin, on, and around each component of the set of transaction entities,but in some embodiments within the set of transaction entities. In someembodiments, the machine learning model 11102 may process the sensordata including the event data 11124 and the state data 11172 to definesimulation data for use by the digital twin system 11120. The machinelearning model 11102 may, for example, receive state data 11172 andevent data 11124 related to a particular transaction entity of theplurality of transaction entities and perform a series of operations onthe state data 11172 and the event data 11124 to format the state data11172 and the event data 11124 into a format suitable for use by thedigital twin system 11120 in the creation of a digital replica of theset of transaction entities. For example, a set of transaction entitiesmay include a die machine configured to place products on a conveyorbelt, the conveyor belt on which the die machine is configured to placethe products, and a plurality of robots configured to add parts to theproducts as they move along the assembly line. The machine learningmodel 11102 may collect data from one or more sensors positioned on,near, in, and/or around each of the die machines, the conveyor belt, andthe plurality of robots. The machine learning model 11102 may performoperations on the sensor data to process the sensor data into simulationdata and output the simulation data to the digital twin system 11120.The digital twin system 11120 simulation may use the simulation data tocreate one or more digital replicas of the die machine, the conveyorbelt, and the plurality of robots, the simulation including for examplemetrics including temperature, wear, speed, rotation, and vibration ofthe die machine, the conveyor belt, and the plurality of robots andcomponents thereof. The simulation may be a substantially real-timesimulation, allowing for a human user of the information technology toview the simulation of the die machine, the conveyor belt, and theplurality of robots, metrics related thereto, and metrics related tocomponents thereof, in substantially real time. The simulation may be apredictive or hypothetical situation, allowing for a human user of theinformation technology to view a predictive or hypothetical simulationof the die machine, the conveyor belt, and the plurality of robots,metrics related thereto, and metrics related to components thereof.

In some embodiments, the machine learning model 11102 may prioritizecollection of sensor data for use in digital replica simulations of oneor more of the transaction entities. The machine learning model 11102may use sensor data and user inputs to train, thereby learning whichtypes of sensor data are most effective for creation of digitalreplicate simulations of one or more of the transaction entities. Forexample, the machine learning model 11102 may find that a particulartransaction entity has dynamic properties such as component wear andthroughput affected by temperature, humidity, and load. The machinelearning model 11102 may, through machine learning, prioritizecollection of sensor data related to temperature, humidity, and load,and may prioritize processing sensor data of the prioritized type intosimulation data for output to the digital twin system 11120. In someembodiments, the machine learning model 11102 may suggest to a user ofthe information technology system that more and/or different sensors ofthe prioritized type be implemented in the information technology nearand around the transaction entity being simulation such that more and/orbetter data of the prioritized type may be used in simulation of thetransaction entity via the digital replica thereof.

In some embodiments, the machine learning model 11102 may be configuredto learn to determine which types of sensor data are to be processedinto simulation data for transmission to the digital twin system 11120based on one or both of a modeling goal and a quality or type of sensordata. A modeling goal may be an objective set by a user of theinformation technology system or may be predicted or learned by themachine learning model 11102. Examples of modeling goals includecreating a digital replica capable of showing dynamics of throughput onan assembly line, which may include collection, simulation, and modelingof, e.g., thermal, electrical power, component wear, and other metricsof a conveyor belt, an assembly machine, one or more products, and othercomponents of the transaction ecosystem. The machine learning model111102 may be configured to learn to determine which types of sensordata are necessary to be processed into simulation data for transmissionto the digital twin system 11120 to achieve such a model. In someembodiments, the machine learning model 11102 may analyze which types ofsensor data are being collected, the quality and quantity of the sensordata being collected, and what the sensor data being collectedrepresents, and may make decisions, predictions, analyses, and/ordeterminations related to which types of sensor data are and/or are notrelevant to achieving the modeling goal and may make decisions,predictions, analyses, and/or determinations to prioritize, improve,and/or achieve the quality and quantity of sensor data being processedinto simulation data for use by the digital twin system 11120 inachieving the modeling goal.

In some embodiments, a user of the information technology system mayinput a modeling goal into the machine learning model 11102. The machinelearning model 11102 may learn to analyze training data to outputsuggestions to the user of the information technology system regardingwhich types of sensor data are most relevant to achieving the modelinggoal, such as one or more types of sensors positioned in, on, or near atransaction entity or a plurality of transaction entities that isrelevant to the achievement of the modeling goal is and/or are notsufficient for achieving the modeling goal, and how a differentconfiguration of the types of sensors, such as by adding, removing, orrepositioning sensors, may better facilitate achievement of the modelinggoal by the machine learning model 11102 and the digital twin system11120. In some embodiments, the machine learning model 11102 mayautomatically increase or decrease collection rates, processing,storage, sampling rates, bandwidth allocation, bitrates, and otherattributes of sensor data collection to achieve or better achieve themodeling goal. In some embodiments, the machine learning model 11102 maymake suggestions or predictions to a user of the information technologysystem related to increasing or decreasing collection rates, processing,storage, sampling rates, bandwidth allocation, bitrates, and otherattributes of sensor data collection to achieve or better achieve themodeling goal. In some embodiments, the machine learning model 11102 mayuse sensor data, simulation data, previous, current, and/or futuredigital replica simulations of one or more transaction entities of theplurality of transaction entities to automatically create and/or proposemodeling goals. In some embodiments, modeling goals automaticallycreated by the machine learning model 11102 may be automaticallyimplemented by the machine learning model 11102. In some embodiments,modeling goals automatically created by the machine learning model 11102may be proposed to a user of the information technology system, andimplemented only after acceptance and/or partial acceptance by the user,such as after modifications are made to the proposed modeling goal bythe user.

In some embodiments, the user may input the one or more modeling goals,for example, by inputting one or more modeling commands to theinformation technology system. The one or more modeling commands mayinclude, for example, a command for the machine learning model 11102 andthe digital twin system 11120 to create a digital replica simulation ofone transaction entity or a set of transaction entities, may include acommand for the digital replica simulation to be one or more of areal-time simulation, and a hypothetical simulation. The modelingcommand may also include, for example, parameters for what types ofsensor data should be used, sampling rates for the sensor data, andother parameters for the sensor data used in the one or more digitalreplica simulations. In some embodiments, the machine learning model11102 may be configured to predict modeling commands, such as by usingprevious modeling commands as training data. The machine learning model11102 may propose predicted modeling commands to a user of theinformation technology system, for example, to facilitate simulation ofone or more of the transaction entities that may be useful for themanagement of the transaction entities and/or to allow the user toeasily identify potential issues with or possible improvements to thetransaction entities. The system of FIG. 111 may include a transactionsmanagement platform and applications.

In some embodiments, the machine learning model 11102 may be configuredto evaluate a set of hypothetical simulations of one or more of thetransaction entities. The set of hypothetical simulations may be createdby the machine learning model 11102 and the digital twin system 11120 asa result of one or more modeling commands, as a result of one or moremodeling goals, one or more modeling commands, by prediction by themachine learning model 11102, or a combination thereof. The machinelearning model 11102 may evaluate the set of hypothetical simulationsbased on one or more metrics defined by the user, one or more metricsdefined by the machine learning model 11102, or a combination thereof.In some embodiments, the machine learning model 11102 may evaluate eachof the hypothetical simulations of the set of hypothetical simulationsindependently of one another. In some embodiments, the machine learningmodel 11102 may evaluate one or more of the hypothetical simulations ofthe set of hypothetical simulations in relation to one another, forexample by ranking the hypothetical simulations or creating tiers of thehypothetical simulations based on one or more metrics.

In some embodiments, the machine learning model 11102 may include one ormore model interpretability systems to facilitate human understanding ofoutputs of the machine learning model 11102, as well as information andinsight related to cognition and processes of the machine learning model11102, i.e., the one or more model interpretability systems allow forhuman understanding of not only “what” the machine learning model 11102is outputting, but also “why” the machine learning model 11102 isoutputting the outputs thereof, and what process led to the machinelearning models 11102 formulating the outputs. The one or more modelinterpretability systems may also be used by a human user to improve andguide training of the machine learning model 11102, to help debug themachine learning model 11102, to help recognize bias in the machinelearning model 11102. The one or more model interpretability systems mayinclude one or more of linear regression, logistic regression, ageneralized linear model (GLM), a generalized additive model (GAM), adecision tree, a decision rule, RuleFit, Naive Bayes Classifier, aK-nearest neighbors algorithm, a partial dependence plot, individualconditional expectation (ICE), an accumulated local effects (ALE) plot,feature interaction, permutation feature importance, a global surrogatemodel, a local surrogate (LIME) model, scoped rules, i.e. anchors,Shapley values, Shapley additive explanations (SHAP), featurevisualization, network dissection, or any other suitable machinelearning interpretability implementation. In some embodiments, the oneor more model interpretability systems may include a model datasetvisualization system. The model dataset visualization system isconfigured to automatically provide to a human user of the informationtechnology system visual analysis related to distribution of values ofthe sensor data, the simulation data, and data nodes of the machinelearning model 11102.

In some embodiments, the machine learning model 11102 may include and/orimplement an embedded model interpretability system, such as a Bayesiancase model (BCM) or glass box. The Bayesian case model uses Bayesiancase-based reasoning, prototype classification, and clustering tofacilitate human understanding of data such as the sensor data, thesimulation data, and data nodes of the machine learning model 11102. Insome embodiments, the model interpretability system may include and/orimplement a glass box interpretability method, such as a Gaussianprocess, to facilitate human understanding of data such as the sensordata, the simulation data, and data nodes of the machine learning model11102.

In some embodiments, the machine learning model 11102 may include and/orimplement testing with concept activation vectors (TCAV). The TCAVallows the machine learning model 11102 to learn human-interpretableconcepts, such as “running,” “not running,” “powered,” “not powered,”“robot,” “human,” “truck,” or “ship” from examples by a processincluding defining the concept, determining concept activation vectors,and calculating directional derivatives. By learning human-interpretableconcepts, objects, states, etc., TCAV may allow the machine learningmodel 11102 to output useful information related to the transactionentities and data collected therefrom in a format that is readilyunderstood by a human user of the information technology system.

In some embodiments, the machine learning model 11102 may be and/orinclude an artificial neural network, e.g. a connectionist systemconfigured to “learn” to perform tasks by considering examples andwithout being explicitly programmed with task-specific rules. Themachine learning model 11102 may be based on a collection of connectedunits and/or nodes that may act like artificial neurons that may in someways emulate neurons in a biological brain. The units and/or nodes mayeach have one or more connections to other units and/or nodes. The unitsand/or nodes may be configured to transmit information, e.g. one or moresignals, to other units and/or nodes, process signals received fromother units and/or nodes, and forward processed signals to other unitsand/or nodes. One or more of the units and/or nodes and connectionstherebetween may have one or more numerical “weights” assigned. Theassigned weights may be configured to facilitate learning, i.e.training, of the machine learning model 11102. The weights assignedweights may increase and/or decrease one or more signals between one ormore units and/or nodes, and in some embodiments may have one or morethresholds associated with one or more of the weights. The one or morethresholds may be configured such that a signal is only sent between oneor more units and/or nodes if a signal and/or aggregate signal crossesthe threshold. In some embodiments, the units and/or nodes may beassigned to a plurality of layers, each of the layers having one or bothof inputs and outputs. A first layer may be configured to receivetraining data, transform at least a portion of the training data, andtransmit signals related to the training data and transformation thereofto a second layer. A final layer may be configured to output anestimate, conclusion, product, or other consequence of processing of oneor more inputs by the machine learning model 11102. Each of the layersmay perform one or more types of transformations, and one or moresignals may pass through one or more of the layers one or more times. Insome embodiments, the machine learning model 11102 may employ deeplearning and being at least partially modeled and/or configured as adeep neural network, a deep belief network, a recurrent neural network,and/or a convolutional neural network, such as by being configured toinclude one or more hidden layers.

In some embodiments, the machine learning model 11102 may be and/orinclude a decision tree, e.g. a tree-based predictive model configuredto identify one or more observations and determine one or moreconclusions based on an input. The observations may be modeled as one ormore “branches” of the decision tree, and the conclusions may be modeledas one or more “leaves” of the decision tree. In some embodiments, thedecision tree may be a classification tree. the classification tree mayinclude one or more leaves representing one or more class labels, andone or more branches representing one or more conjunctions of featuresconfigured to lead to the class labels. In some embodiments, thedecision tree may be a regression tree. The regression tree may beconfigured such that one or more target variables may take continuousvalues.

In some embodiments, the machine learning model 11102 may be and/orinclude a support vector machine, e.g. a set of related supervisedlearning methods configured for use in one or both of classification andregression-based modeling of data. The support vector machine may beconfigured to predict whether a new example falls into one or morecategories, the one or more categories being configured during trainingof the support vector machine.

In some embodiments, the machine learning model 11102 may be configuredto perform regression analysis to determine and/or estimate arelationship between one or more inputs and one or more features of theone or more inputs. Regression analysis may include linear regression,wherein the machine learning model 11102 may calculate a single line tobest fit input data according to one or more mathematical criteria.

In embodiments, inputs to the machine learning model 11102 (such as aregression model, Bayesian network, supervised model, or other type ofmodel) may be tested, such as by using a set of testing data that isindependent from the data set used for the creation and/or training ofthe machine learning model, such as to test the impact of various inputsto the accuracy of the model 11102. For example, inputs to theregression model may be removed, including single inputs, pairs ofinputs, triplets, and the like, to determine whether the absence ofinputs creates a material degradation of the success of the model 11102.This may assist with recognition of inputs that are in fact correlated(e.g., are linear combinations of the same underlying data), that areoverlapping, or the like. Comparison of model success may help selectamong alternative input data sets that provide similar information, suchas to identify the inputs (among several similar ones) that generate theleast “noise” in the model, that provide the most impact on modeleffectiveness for the lowest cost, or the like. Thus, input variationand testing of the impact of input variation on model effectiveness maybe used to prune or enhance model performance for any of the machinelearning systems described throughout this disclosure.

In some embodiments, the machine learning model 11102 may be and/orinclude a Bayesian network. The Bayesian network may be a probabilisticgraphical model configured to represent a set of random variables andconditional independence of the set of random variables. The Bayesiannetwork may be configured to represent the random variables andconditional independence via a directed acyclic graph. The Bayesiannetwork may include one or both of a dynamic Bayesian network and aninfluence diagram.

In some embodiments, the machine learning model 11102 may be defined viasupervised learning, i.e. one or more algorithms configured to build amathematical model of a set of training data containing one or moreinputs and desired outputs. The training data may consist of a set oftraining examples, each of the training examples having one or moreinputs and desired outputs, i.e. a supervisory signal. Each of thetraining examples may be represented in the machine learning model 11102by an array and/or a vector, i.e. a feature vector. The training datamay be represented in the machine learning model 11102 by a matrix. Themachine learning model 11102 may learn one or more functions viaiterative optimization of an objective function, thereby learning topredict an output associated with new inputs. Once optimized, theobjective function may provide the machine learning model 11102 with theability to accurately determine an output for inputs other than inputsincluded in the training data. In some embodiments, the machine learningmodel 11102 may be defined via one or more supervised learningalgorithms such as active learning, statistical classification,regression analysis, and similarity learning. Active learning mayinclude interactively querying, by the machine learning model 11102, auser and/or an information source to label new data points with desiredoutputs. Statistical classification may include identifying, by themachine learning model 11102, to which a set of subcategories, i.e.subpopulations, a new observation belongs based on a training set ofdata containing observations having known categories. Regressionanalysis may include estimating, by the machine learning model 11102relationships between a dependent variable, i.e. an outcome variable,and one or more independent variables, i.e. predictors, covariates,and/or features. Similarity learning may include learning, by themachine learning model 11102, from examples using a similarity function,the similarity function being designed to measure how similar or relatedtwo objects are.

In some embodiments, the machine learning model 11102 may be defined viaunsupervised learning, i.e. one or more algorithms configured to build amathematical model of a set of data containing only inputs by findingstructure in the data such as grouping or clustering of data points. Insome embodiments, the machine learning model 11102 may learn from testdata, i.e. training data, that has not been labeled, classified, orcategorized. The unsupervised learning algorithm may includeidentifying, by the machine learning model 11102, commonalities in thetraining data and learning by reacting based on the presence or absenceof the identified commonalities in new pieces of data. In someembodiments, the machine learning model 11102 may generate one or moreprobability density functions. In some embodiments, the machine learningmodel 11102 may learn by performing cluster analysis, such as byassigning a set of observations into subsets, i.e. clusters, accordingto one or more predesignated criteria, such as according to a similaritymetric of which internal compactness, separation, estimated density,and/or graph connectivity are factors.

In some embodiments, the machine learning model 11102 may be defined viasemi-supervised learning, i.e. one or more algorithms using trainingdata wherein some training examples may be missing training labels. Thesemi-supervised learning may be weakly supervised learning, wherein thetraining labels may be noisy, limited, and/or imprecise. The noisy,limited, and/or imprecise training labels may be cheaper and/or lesslabor intensive to produce, thus allowing the machine learning model11102 to train on a larger set of training data for less cost and/orlabor.

In some embodiments, the machine learning model 11102 may be defined viareinforcement learning, such as one or more algorithms using dynamicprogramming techniques such that the machine learning model 11102 maytrain by taking actions in an environment in order to maximize acumulative reward. In some embodiments, the training data is representedas a Markov Decision Process.

In some embodiments, the machine learning model 11102 may be defined viaself-learning, wherein the machine learning model 11102 is configured totrain using training data with no external rewards and no externalteaching, such as by employing a Crossbar Adaptive Array (CAA). The CAAmay compute decisions about actions and/or emotions about consequencesituations in a crossbar fashion, thereby driving teaching of themachine learning model 11102 by interactions between cognition andemotion.

In some embodiments, the machine learning model 11102 may be defined viafeature learning, i.e. one or more algorithms designed to discoverincreasingly accurate and/or apt representations of one or more inputsprovided during training, e.g. training data. Feature learning mayinclude training via principal component analysis and/or clusteranalysis. Feature learning algorithms may include attempting, by themachine learning model 11102, to preserve input training data while alsotransforming the input training data such that the transformed inputtraining data is useful. In some embodiments, the machine learning model11102 may be configured to transform the input training data prior toperforming one or more classifications and/or predictions of the inputtraining data. Thus, the machine learning model 11102 may be configuredto reconstruct input training data from one or more unknowndata-generating distributions without necessarily conforming toimplausible configurations of the input training data according to thedistributions. In some embodiments, the feature learning algorithm maybe performed by the machine learning model 11102 in a supervised,unsupervised, or semi-supervised manner.

In some embodiments, the machine learning model 11102 may be defined viaanomaly detection, i.e. by identifying rare and/or outlier instances ofone or more items, events and/or observations. The rare and/or outlierinstances may be identified by the instances differing significantlyfrom patterns and/or properties of a majority of the training data.Unsupervised anomaly detection may include detecting of anomalies, bythe machine learning model 11102, in an unlabeled training data setunder an assumption that a majority of the training data is “normal.”Supervised anomaly detection may include training on a data set whereinat least a portion of the training data has been labeled as “normal”and/or “abnormal.”

In some embodiments, the machine learning model 11102 may be defined viarobot learning. Robot learning may include generation, by the machinelearning model 11102, of one or more curricula, the curricula beingsequences of learning experiences, and cumulatively acquiring new skillsvia exploration guided by the machine learning model 11102 and socialinteraction with humans by the machine learning model 11102. Acquisitionof new skills may be facilitated by one or more guidance mechanisms suchas active learning, maturation, motor synergies, and/or imitation.

In some embodiments, the machine learning model 11102 can be defined viaassociation rule learning. Association rule learning may includediscovering relationships, by the machine learning model 11102, betweenvariables in databases, in order to identify strong rules using somemeasure of “interestingness.” Association rule learning may includeidentifying, learning, and/or evolving rules to store, manipulate and/orapply knowledge. The machine learning model 11102 may be configured tolearn by identifying and/or utilizing a set of relational rules, therelational rules collectively representing knowledge captured by themachine learning model 11102. Association rule learning may include oneor more of learning classifier systems, inductive logic programming, andartificial immune systems. Learning classifier systems are algorithmsthat may combine a discovery component, such as one or more geneticalgorithms, with a learning component, such as one or more algorithmsfor supervised learning, reinforcement learning, or unsupervisedlearning. Inductive logic programming may include rule-learning, by themachine learning model 11102, using logic programming to represent oneor more of input examples, background knowledge, and hypothesisdetermined by the machine learning model 11102 during training. Themachine learning model 11102 may be configured to derive a hypothesizedlogic program entailing all positive examples given an encoding of knownbackground knowledge and a set of examples represented as a logicaldatabase of facts.

Referring to FIG. 112, a compliance system 11200 that facilitates thelicensing of personality rights using a distributed ledger andcryptocurrency is depicted. As used herein, personality rights may referto an entity's ability to control the use of his, her, or its identityfor commercial purposes. The term entity, as used herein, may refer toan individual or an organization (e.g., a university, a school, a team,a corporation, or the like) that agrees to license its personalityrights, unless context suggests otherwise. This may include an entity'sability to control the use of its name, image, likeness, voice, or thelike. For example, an individual exercising their personality rights forcommercial purposes may include appearing in a commercial, televisionshow, or movie, making a sponsored social media post (e.g., Instagrampost, Facebook post, Twitter tweet, or the like), having their nameappear on clothing (e.g., a jersey, t-shirts, sweatshirts, or the like)or other goods, appearing in a video game, or the like. In embodiments,individuals may refer to student athletes or professional athletes, butmay include other classes of individuals as well. While the currentdescription makes reference to the NCAA, the system may be used tomonitor and facilitate transactions relating to other individuals andorganizations. For example, the system may be used in the context ofprofessional sports, where organizations may use sponsorships and otherlicensing deals to circumvent salary caps or other league rules (e.g.,FIFA fair play rules).

In embodiments, the compliance system 11200 maintains one or moredigital ledgers that record transactions relating to the licensing ofpersonality rights of entities. In embodiments, a digital ledger may bea distributed ledger that is distributed amongst a set of computingdevices 11270, 11280, 11290 (also referred to as nodes) and/or may beencrypted. Put another way, each participating node may store a copy ofthe distributed ledger. An example of the digital ledger is a Blockchainledger. In some embodiments, a distributed ledger is stored across a setof public nodes. In other embodiments, a distributed ledger is storedacross a set of whitelisted participant nodes (e.g., on the servers ofparticipating universities or teams). In some embodiments, the digitalledger is privately maintained by the compliance system 11200. Thelatter configuration provides a more energy efficient means ofmaintaining a digital ledger; while the former configurations (e.g.,distributed ledgers) provide a more secure/verifiable means ofmaintaining a digital ledger.

In embodiments, a distributed ledger may store tokens. The tokens may becryptocurrency tokens that are transferrable to licensors and licensees.In some embodiments, a distributed ledger may store the ownership dataof each token. A token (or a portion thereof) may be owned by thecompliance system, the governing organization (e.g., the NCAA), alicensor, a licensee, a team, an institution, an individual or the like.In embodiments, the distributed ledger may store event records. Eventrecords may store information relating to events associated with theentities involved with the compliance system. For example, an eventrecord may record an agreement entered into by two parties, thecompletion of an obligation by a licensor, the distribution of funds toa licensor from a license, the non-completion of an obligation by alicensor, the distribution of funds to entities associated with thelicensee (e.g., teammates, institution, team, etc.), and the like.

In embodiments, the digital ledger may store smart contracts that governagreements between licensors and licensees. As used herein, a licenseemay be an organization or person that wishes to enter an agreement tolicense a licensor's personality rights. Examples of licensees mayinclude, but are not limited to, a car dealership that wants a starstudent athlete to appear in a print ad, a company that wants thelikeness of a licensor (e.g., an athlete and/or a team) to appear in acommercial, a video game maker that wants to use team names, teamapparel, player names and/or numbers in a video game, a shoe maker thatwants an athlete to endorse a sneaker, a television show producer thatwants an athlete to appear in the television show, or the like. Inembodiments, the compliance system 11200 generates a smart contract thatmemorializes an agreement between the individual and a licensee andfacilitates the transfer of consideration (e.g., money) when the partiesagree that the individual has performed his or her requirements as putforth in the agreement. For example, an athlete may agree to appear in acommercial on behalf of a local car dealership. The smart contract inthis example may include an identifier of the athlete (e.g., anindividual ID and/or an individual account ID), an identifier of theorganization (e.g., an organization ID and/or an organization accountID), the requirements of the individual (e.g., to appear in acommercial, to make a sponsored social media post, to appear at anautograph signing, or the like), and the consideration (e.g., a monetaryamount). In embodiments, the smart contract may include additionalterms. In embodiments, the additional terms may include an allocationrule that defines a manner by which the consideration is allocated tothe athlete and one or more other parties (e.g., agent, manager,university, team, teammates, or the like). For example, in the contextof a student athlete, a smart contract may define a split between thelicensing athlete, the athletic department of the student athlete'suniversity, and the student athlete's teammates. In a specific example,a university may have a policy that requires a player appearing in anyadvertisement to split the funds according to a 60/20/20 split, whereby60% of the funds are allocated to the student athlete appearing in thecommercial, 20% of the funds are allocated to the athletic department,and 20% of the funds are allocated to the student athlete's teammates.When a smart contract verifies that the athlete has performed his or herduties with respect to the smart contract (e.g., appeared for thecommercial), the smart contract can transfer the agreed upon amount froman account of the licensee to an account of the athlete and accounts ofany other entities that may be allocated a percentage of the funds inthe smart contract (e.g., athletic department and teammates).

In embodiments, the compliance system 11200 utilizes cryptocurrency tofacilitate the transfer of funds. In embodiments, the cryptocurrency ismined by participant nodes and/or generated by the compliance system.The cryptocurrency can be an established type of cryptocurrency (e.g.,Bitcoin, Ethereum, Litecoin, or the like) or may be a proprietarycryptocurrency. In some embodiments, the cryptocurrency is a peggedcryptocurrency that is pegged to a particular fiat currency (e.g.,pegged to the US dollar. British Pound, Euro, or the like). For example,a single unit of cryptocurrency (also referred to as a “coin”) may bepegged to a single unit of fiat currency (e.g., a US dollar). Inembodiments, a licensee may exchange fiat currency for a correspondingamount of cryptocurrency. For example, if the cryptocurrency is peggedto the dollar, the licensee may exchange an amount of US dollars for acorresponding amount of cryptocurrency. In embodiments, the compliancesystem 11200 may keep a percentage of the real-world currency as atransaction fee (e.g., 5%). For example, in exchanging $10,000, thecompliance system 11200 may distribute $9,500 dollars' worth ofcryptocurrency to an account of the licensee and may keep the $5,000dollars as a transaction fee. Once the cryptocurrency is deposited in anaccount of a licensee, the licensee may enter into transactions withindividuals.

In embodiments, the compliance system 11200 may allow organizations tocreate smart contract templates that define one or moreconditions/restrictions on the contract. For example, an organizationmay predefine the allocation between the licensee, the organization, andany other individuals (e.g., coaches, teammates, representatives).Additionally or alternatively, the organization may place minimum and/ormaximum amounts of agreements. Additionally or alternatively, theorganization may place restrictions on when an agreement can be enteredinto and/or performed. For example, players may be restricted fromappearing in commercials or advertisements during the season and/orduring exam periods. These details may be stored in an organizationdatastore 11256A Organizations may place other conditions/restrictionsin a smart contract. In these embodiments, an individual and licenseewishing to enter to an agreement must use a smart contract templateprovided by the organization to which the individual belongs. In otherwords, the compliance system 11200 may only allow an individual that hasan active relationship with an organization (e.g., plays on a team of auniversity) to participate in a smart contract if the smart contract isdefined by or otherwise approved by the organization.

In embodiments, the compliance system 11200 manages a clearinghouseprocess that approves potential licensees. Before a licensee canparticipate in agreements facilitated by the compliance system 11200,the licensee can provide information relating to the licensee. This mayinclude a tax ID number, an entity name, incorporation information(e.g., state and type), a list of key personnel (e.g., directors,executives, board members, approved decision makers, and/or the like),and any other suitable information. In embodiments, the potentiallicensee may be required to sign (e.g., eSign or wet ink signature) adocument indicating that the organization will not willingly use thecompliance system 11200 to circumvent any rules, laws, or regulations(e.g., they will not circumvent NCAA regulations). In embodiments, thecompliance system 11200 or another entity (e.g., the NCAA) may verifythe licensee. Once verified, the information is stored in a licenseedatastore 11256B and the licensee may participate in transactions.

In embodiments, the compliance system 11200 may create accounts forlicensors once they have joined an organization (e.g., signed anathletic scholarship with a university). Once a licensor is verified asbeing affiliated with the organization, the compliance system 11200 maycreate an account for the licensor and may create a relationship betweenthe individual and the organization, whereby the licensor may berequired to use smart contracts that are approved or provided by theorganization. Should the licensor join another organization (e.g.,transfers to another school), the compliance system 11200 may sever therelationship with the previous organization and may create a newrelationship with the other organization. Similarly, once a licensor isno longer affiliated with any organization (e.g., the player graduates,enters a professional league, retires, or the like), the compliancesystem 11200 may prevent the licensor from participating in transactionson the compliance system 11200.

In embodiments, the compliance system 11200 may provide a graphical userinterface that allows users to create smart contracts governingpersonality rights licenses. In these embodiments, the compliance systemallows a user (e.g., a licensor) to select a smart contract template. Insome embodiments, the compliance system 11200 may restrict the user toonly select a smart contract template that is associated with aninstitution of the licensor. In embodiments, the graphical userinterface allows a user to define certain terms (e.g., the type or typesof obligations placed on the licensor, an amount of funds to paid, adate by which the obligations of the licensor must be completed by, alocation at which the obligation is completed, and/or other suitableterms). Upon a user providing input for parameterizing a smart contracttemplate, the compliance system 11200 may generate a smart contract byparameterizing one or more variables in the smart contract with theprovided input. Upon parameterizing an instance of a smart contract, thecompliance system 11200 may deploy the smart contract. In someembodiments, the compliance system 11200 may deploy the smart contractby broadcasting the parameterized smart contract to the participantnodes, which in turn may update each respective instance of thedistributed ledger with the new smart contract. In some embodiments, aninstitution of the licensor must approve the parameterized smartcontract before the parameterized smart contract may be deployed to thedistributed ledger.

In embodiments, the compliance system 11200 may provide a graphical userinterface to verify performance of an obligation by a licensor. In someof these embodiments, the compliance system 11200 may include anapplication that is accessed by licensors, that allows a licensor toprove that he or she performed an obligation. In some of theseembodiments, the application may allow a user to record locations thatthe licensor went to (e.g., locations of film or photo shoots), toupload records (e.g., screen shots of social media posts) or to provideother corroborating evidence that the licensor has performed his or herobligations with respect to a licensing transaction. In this way, thelicensor can prove that he or she performed the tasks required by thelicensing deal. In some embodiments, the application may interact with awearable device or may capture other digital exhaust, such as socialmedia posts of the user (e.g., licensor) to collect evidence thatsupports or disproves a licensor's claim that he or she performed theobligations under the transaction agreement. In embodiments, thecorroborating evidence collected by the application may be recorded bythe application and stored on the distributed ledger as a licensordatastore 11256C.

In embodiments, the compliance system 11200 (or a smart contract issuedin connection with the compliance system 11200) may completetransactions pertaining to a smart contract governing the licensing ofthe personality rights of a licensor upon verification that licensor hasperformed his or her obligations defined in the agreement. As mentioned,the licensor may use an application to provide evidence of satisfactionof the obligations of the agreement. Additionally or alternatively, thelicensee may provide verification that the licensor has performed his orher obligations (e.g., using an application). In embodiments, the smartcontract governing the agreement may receive verification that thelicensor has performed his or her obligations defined by the agreement.In response the smart contract may release (or initiate the release of)the cryptocurrency amount defined in the smart contract. Thecryptocurrency amount may be distributed to the accounts of the licensorand any other parties defined in the agreement (e.g., teammates of thelicensor, the program of the licensor, the regulating body, or thelike).

In embodiments, the compliance system 11200 is configured to performanalytics and provide reports to a regulatory body and/or other entities(e.g., the other organizations). In these embodiments, the analytics maybe used to identify individuals that are potentially circumventing therules and regulations of the regulatory body. Furthermore, in someembodiments, transaction records may be maintained on a distributedledger, whereby different organizations may be able to view agreementsentered into by individuals affiliated with other organizations suchthat added levels of transparency and oversight may disincentivizeindividuals, organizations, and/or licensees from circumventing rulesand regulations.

In embodiments, the compliance system 11200 may train and/or leveragemachine-learned models to identify potential instances of circumventionof rules or regulations. In these embodiments, the compliance system11200 may train machine-learned models using outcome data. Examples ofoutcome data may include data relating to a set of transactions where anorganization (e.g., a team or university), licensee (e.g., a company),and/or licensor (e.g., an athlete) were determined to be circumventingrules or regulations and data relating to a set of transactions where anorganization, licensee, and/or licensor were found to be in compliancewith the rules and regulations. Examples of machine-learned modelsinclude neural networks, regression-based models, decisions trees,random forests, Hidden Markov Models, Bayesian Models, and the like. Inembodiments, the compliance system 11200 may leverage a machine-learnedmodel by obtaining a set of records relating to transactions a licensee,a licensor, and/or an organization (e.g., a team or university) from thedistributed ledger. The compliance system may extract relevant features,such as the amount paid to a particular licensor by a licensee, amountspaid to other licensors on other teams, affiliations of the licensor,amounts paid to a licensor by other licensees, and the like, and mayfeed the features to the machine-learned model.

The machine-learned model may issue a score that indicates a likelihoodthat the transaction was legitimate (or illegitimate) based on theextracted features. In embodiments, the compliance system 11200 mayprovide notifications to relevant parties (e.g., regulators) when theoutput of a machine-learned model indicates that a transaction waslikely illegitimate.

FIG. 113 illustrates an example system 11300 configured forelectronically facilitating licensing of one or more personality rightsof a licensor, in accordance with some embodiments of the presentdisclosure. In some embodiments, the system 11300 may include one ormore computing platforms 11302. Computing platform(s) 11302 may beconfigured to communicate with one or more remote platforms 11304according to a client/server architecture, a peer-to-peer architecture,and/or other architectures. Remote platform(s) 11304 may be configuredto communicate with other remote platforms via computing platform(s)11302 and/or according to a client/server architecture, a peer-to-peerarchitecture, and/or other architectures. Users may access system 11300via remote platform(s) 11304.

In embodiments, computing platform(s) 11302 may be configured bymachine-readable instructions 11306. Machine-readable instructions 11306may include one or more instruction modules. The instruction modules mayinclude computer program modules. The instruction modules may includeone or more of an access module 11208, a fund management module 11212, aledger management module 11216, a verification module 11218, ananalytics module 11220, and/or other instruction modules.

In embodiments, the access module 11208 may be configured to receive anaccess request from a licensee to obtain approval to license personalityrights from a set of available licensors. In embodiments, the accessmodule 11208 may be configured to selectively grant access to thelicensee based on the access request. For example, the access module11208 may receive a name of a potential licensee (e.g., corporate name),a list of principals (e.g., executives and/or owners) of the potentiallicensee, a location of the licensee, affiliations of the licensee andthe principals thereof, and the like. In embodiments, the access module11208 may provide this information to a human that grants access and/ormay feed this information into an artificial intelligence system thatvets potential licensees. In embodiments, the access module 11208 isconfigured to selectively grant access to a licensor by verifying thatthe licensee is permitted to engage with a set of licensors includingthe licensor based on the set of affiliations. Selectively grantingaccess to the licensor may include, in response to verifying that thelicensee is permitted to engage with the set of licensors, granting thelicensee approval to engage with the set of licensees. The set ofaffiliations of the licensee may include organizations to which thelicensee or a principal associated with the licensee donates to or owns.

In embodiments, the fund management module 11212 may be configured toreceive confirmation of a deposit of an amount of funds from thelicensee. In some embodiments, the fund management module 11212 may beconfigured to issue an amount of cryptocurrency corresponding to theamount of funds deposited by the licensee to an account of the licensee.In embodiments, the fund management system 11212 may be configured toescrow the consideration amount of cryptocurrency from the account ofthe licensee until the funds are released by a smart contract.

In embodiments, the ledger management module 11216 may be configured toreceive a smart contract request to create a smart contract governingthe licensing of the one or more personality rights of the licensor bythe licensee. In embodiments, the ledger management module 11216 may beconfigured to generate the smart contract based on the smart contractrequest. The smart contract may be generated using a smart contracttemplate provided by an interested third party (e.g., a university, agoverning body, or the like) and by one or more parameters provided by auser (e.g., the licensor, the team of the licensor, an institution,and/or licensee) By way of non-limiting example, the interested thirdparty may be one of a university, a sports team, or a collegiateathletics governance organization. The smart contract request mayindicate one or more terms including a consideration amount ofcryptocurrency to be paid to the licensor in exchange for one or moreobligations on the licensor. In embodiments, the ledger managementmodule 11216 may be configured to deploy the smart contract to adistributed ledger. The distributed ledger may be auditable by a set ofthird parties, including the interested third party. The distributedledger may be a public ledger. The distributed ledger may be a privateledger that is only hosted on computing devices associated withinterested third parties. In embodiments, the distributed ledger may bea blockchain.

In embodiments, the verification module 11218 may be configured toverify that the licensor has performed the one or more obligation. Insome embodiments, verifying that a licensor has performed the one ormore obligations may include receiving location data from a wearabledevice associated with the licensor and verifying that the licensor hasperformed the one or more obligations based on the location data,whereby the location may be used to show that the licensor was at aparticular location at a particular time (e.g., a photoshoot or afilming). In embodiments, verifying that the licensor may have performedthe one or more obligations includes receiving social media data from asocial media website and verifying that the licensor has performed theone or more obligations based on the social media data, whereby thesocial media data may be used to show that the licensor has made arequired social media posting. In embodiments, verifying that thelicensor may have performed the one or more obligations includesreceiving media content from an external data source and verifying thatthe licensor has performed the one or more obligations based on themedia content, whereby a licensor and/or licensee may upload the mediacontent to prove that the licensor has appeared in the media content. Byway of non-limiting example, the media content may be one of a video, aphotograph, or an audio recording. In embodiments, the verificationmodule 11218 may generate and output an event record to theparticipating nodes upon verifying that a licensor has performed itsobligations. In embodiments, the verification module 11218 may generateand output an event record to the participating nodes that indicatesthat the compliance system 11200 has received corroborating evidence(e.g., social media data, location data, and/or media contents) thatshow that the licensor has performed his or her obligations. Inembodiments, the verification module 11218 may be configured to outputan event record indicating completion of a licensing transaction definedby the smart contract to the distributed ledger.

In embodiments, the verification module 11218 may be configured toverify, by the smart contract, that the licensor has performed the oneor more obligations. In embodiments, the verification module 11218and/or a smart contract may be configured to, in response to receivingverification that the licensor has performed the one or moreobligations, release at least a portion of the consideration amount ofcryptocurrency into a licensor account of the licensor. Releasing the atleast a portion of the consideration amount of cryptocurrency into alicensee account of the licensee may include identifying an allocationsmart contract associated with the licensee and distributing theconsideration amount of the cryptocurrency in accordance with theallocation rules. By way of non-limiting example, the additionalentities may include one or more of teammates of the licensor, coachesof the licensor, a team of the licensor, a university of the licensee,and a governing body (e.g., the NCAA).

In embodiments, an analytics module 11220 may be configured to obtain aset of records indicating completion of a set of respective transactionsfrom the distributed ledger. The set of records may include the recordindicating the completion of the transaction defined by the smartcontract. In embodiments, the analytics module 11220 may be configuredto determine whether an organization associated with the licensor islikely in violation of one or more regulations based on the set ofrecords and a fraud detection model. The fraud detection model may betrained using training data that indicates permissible transactions andfraudulent transactions.

In some implementations, the allocation smart contract may defineallocation rules governing a manner by which funds resulting fromlicensing the one or more personality rights are to be distributedamongst the licensor and one or more additional entities.

In some implementations, by way of non-limiting example, the regulationsmay be provided by one of NCAA, FIFA, NBA, MLB, NFL, MLS, NHL, and thelike.

In some implementations, computing platform(s) 11302, remote platform(s)11304, and/or external resources 11334 may be operatively linked via oneor more electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which computing platform(s)11302, remote platform(s) 11304, and/or external resources 11334 may beoperatively linked via some other communication media.

A given remote platform 11304 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable an expert or user associated withthe given remote platform 11304 to interface with compliance system11200 and/or external resources 11334, and/or provide otherfunctionality attributed herein to remote platform(s). 11304. By way ofnon-limiting example, a given remote platform 11304 and/or a givencomputing platform 11302 may include one or more of a server, a desktopcomputer, a laptop computer, a handheld computer, a tablet computingplatform, a Netbook, a Smartphone, a gaming console, and/or othercomputing platforms.

External resources 11334 may include sources of information outside ofcompliance system 11200, external entities participating with compliancesystem 11200, and/or other resources. In some implementations, some orall of the functionality attributed herein to external resources 11334may be provided by resources included in compliance system 11200.

Computing platform(s) 202 may include electronic storage 11336, one ormore processors 11338, and/or other components. Computing platform(s)1202 may include communication lines, or ports to enable the exchange ofinformation with a network and/or other computing platforms.Illustration of computing platform(s) 11302 in FIG. 113 is not intendedto be limiting. Computing platform(s) 11302 may include a plurality ofhardware, software, and/or firmware components operating together toprovide the functionality attributed herein to computing platform(s)11302. For example, computing platform(s) 11302 may be implemented by acloud of computing platforms operating together as computing platform(s)11302.

Electronic storage 11336 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 11336 may include one or both of system storage thatis provided integrally (i.e., substantially non-removable) withcomputing platform(s) 11302 and/or removable storage that is removablyconnectable to computing platform(s) 11302 via, for example, a port(e.g., a USB port, a firewire port, etc.) or a drive (e.g., a diskdrive, etc.). Electronic storage 11336 may include one or more ofoptically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. Electronic storage11336 may include one or more virtual storage resources (e.g., cloudstorage, a virtual private network, and/or other virtual storageresources). Electronic storage 11336 may store software algorithms,information determined by processor(s) 11338, information received fromcomputing platform(s) 11302, information received from remoteplatform(s) 11304, and/or other information that enables computingplatform(s) 11302 to function as described herein.

Processor(s) 11338 may be configured to provide information processingcapabilities in computing platform(s) 11302. As such, processor(s) 11338may include one or more of a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information. Althoughprocessor(s) 11338 is shown in FIG. 113 as a single entity, this is forillustrative purposes only. In some implementations, processor(s) 11338may include a plurality of processing units. These processing units maybe physically located within the same device, or processor(s) 11338 mayrepresent processing functionality of a plurality of devices operatingin coordination. Processor(s) 11338 may be configured to execute modules11208, 11212, 11216, 11218, 11220, and/or other modules. Processor(s)11338 may be configured to execute modules 11208, 11212, 11216, 11218,11220, and/or other modules by software; hardware; firmware; somecombination of software, hardware, and/or firmware; and/or othermechanisms for configuring processing capabilities on processor(s)11338. As used herein, the term “module” may refer to any component orset of components that perform the functionality attributed to themodule. This may include one or more physical processors duringexecution of processor readable instructions, the processor readableinstructions, circuitry, hardware, storage media, or any othercomponents.

It should be appreciated that although modules 11208, 11212, 11216,11218, and 11220 are illustrated in FIG. 113 as being implemented withina single processing unit, in implementations in which processor(s) 11338includes multiple processing units, one or more of modules 11208, 11212,11216, 11218, and 11220 may be implemented remotely from the othermodules. The description of the functionality provided by the differentmodules 11208, 11212, 11216, 11218, and 11220 described below is forillustrative purposes, and is not intended to be limiting, as any ofmodules 11208, 11212, 11216, 11218, and/or 11220 may provide more orless functionality than is described. For example, one or more ofmodules 11208, 11212, 11216, 11218, and/or 11220 may be eliminated, andsome or all of its functionality may be provided by other ones ofmodules 11208, 11212, 11216, 11218, and/or 11220. As another example,processor(s) 11338 may be configured to execute one or more additionalmodules that may perform some or all of the functionality attributedbelow to one of modules 11208, 11212, 11216, 11218, and/or 11220.

FIGS. 114 and/or 115 illustrates an example method 11400 forelectronically facilitating licensing of one or more personality rightsof a licensor, in accordance with some embodiments of the presentdisclosure. The operations of method 11400 presented below are intendedto be illustrative. In some embodiments, method 11400 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of method 11400 are illustrated inFIGS. 114 and/or 115 and described below is not intended to be limiting.

In some implementations, method 11400 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 11400 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 11400.

FIG. 114 illustrates method 11400, in accordance with one or moreimplementations of the present disclosure.

At 11402, the method includes receiving an access request from alicensee to obtain approval to license personality rights from a set ofavailable licensors. Operation 11402 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to access module11208, in accordance with one or more implementations.

At 11404, the method includes selectively granting access to thelicensee based on the access request. Operation 11404 may be performedby one or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar to accessmodule 11208, in accordance with one or more implementations.

At 11406, the method includes receiving confirmation of a deposit of anamount of funds from the licensee. Operation 11406 may be performed byone or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar to fundmanagement module 11212, in accordance with one or more implementations.

At 11408, the method includes issuing an amount of cryptocurrencycorresponding to the amount of funds deposited by the licensee to anaccount of the licensee. Operation 11408 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to fund managementmodule 11212, in accordance with one or more implementations.

FIG. 115 illustrates method 11500, in accordance with one or moreimplementations of the present disclosure.

At 11522, the method includes receiving a smart contract request tocreate a smart contract governing the licensing of the one or morepersonality rights of the licensor by the licensee. The smart contractrequest may indicate one or more terms including a consideration amountof cryptocurrency to be paid to the licensor in exchange for one or moreobligations on the licensor. Operation 11522 may be performed by one ormore hardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to the ledgermanagement module 11216, in accordance with one or more implementations.

At 11524, the method includes generating the smart contract based on thesmart contract request. Operation 11524 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to ledger managementmodule 11216, in accordance with one or more implementations.

At 11526, the method includes escrowing the consideration amount ofcryptocurrency from the account of the licensee. Operation 11526 may beperformed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to fund management module 11212, in accordance with one or moreimplementations.

At 11528, the method includes deploying the smart contract to adistributed ledger. Operation 11528 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to ledger managementmodule 11216, in accordance with one or more implementations.

At 11530, the method includes verifying, by the smart contract, that thelicensor has performed the one or more obligations. Operation 11530 maybe performed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to verification module 11218, in accordance with one or moreimplementations.

At 11532, the method includes in response to receiving verification thatthe licensor has performed the one or more obligations, releasing atleast a portion of the consideration amount of cryptocurrency into alicensor account of the licensor. Operation 11532 may be performed byone or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar to theverification module 11218, in accordance with one or moreimplementations.

At 11534, the method includes outputting a record indicating acompletion of a licensing transaction defined by the smart contract tothe distributed ledger. Operation 11534 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to the verificationmodule 11218 and/or the ledger management module 11216, in accordancewith one or more implementations.

FIG. 116 illustrates method 11600, in accordance with one or moreimplementations.

At 11602, the method includes obtaining a set of records indicatingcompletion of a set of respective transactions from the distributedledger. The set of records may include the record indicating thecompletion of the transaction defined by the smart contract. Operation11602 may be performed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to the analytics module 11220, in accordance with one or moreimplementations.

At 11604, the method includes determining whether an organizationassociated with the licensor is likely in violation of one or moreregulations based on the set of records and a fraud detection model.Operation 11604 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to the analytics module 11220, in accordance withone or more implementations.

Referring to FIG. 117, a computer-implemented method 11700 for selectingan AI solution for use in a robotic or automated process is depicted.The computer-implemented method may include receiving one or morefunctional media 11702. The functional media may include informationindicative of brain activity of a worker engaged in a task to beautomated. The functional media may be functional imaging, such an MRI,an FMRI, and the like from which an area of neocortex activity may beidentified. The functional media may be an image, a video stream, anaudio stream, and the like, from which a type of brain activity may beinferred. The functional media may be acquired while the worker isperforming the work or while performing a simulation of the work, forexample in an augmented reality, a virtual reality environment, or on amodel of the equipment and/or environment. After being received, thefunctional media(s) are analyzed 11704 to identify an activity level inat least one brain region 11706. Based on the activity level, a brainregion parameter and/or an activity parameter are identified 11708. Thebrain region parameter may represent a specific region of the neocortexsuch as frontal, parietal, occipital, and temporal lobes of theneocortex, including primary visual cortex and the primary auditorycortex, or subdivisions of the neocortex, including ventrolateralprefrontal cortex (Broca's area), and orbitofrontal cortex. The activityparameter may represent functional areas of the brain, such as visualprocessing, inductive reasoning, audio processing, olfactory processing,muscle control, and the like. An activity parameter may berepresentative of a type of activity in which the worker is engaged suchas visual processing (looking) audio processing (listening), olfactoryprocessing (smelling), motion activity, listening to the sound of theequipment, watching another negotiator, and the like. An activity levelmay be representative of a strength or level of activity, such as anextent of the brain region involved, a signal strength, whether a brainregion is engaged or unengaged, and the like.

Based on one or more of the brain region parameter, the activityparameter, or the activity level, an action parameter may be identified11710. An action parameter may provide additional information regardingthe activity parameter. For example, activity parameter is indicative ofmotion, an action parameter may describe a range of motion, a speed ofmotion, a repetition of motion, a use of muscle memory, a smoothness ofmotion, a flow of motion, a timing of motion, and the like. Based on oneor more of the brain region parameter, the activity parameter, or theactivity level, a component to be incorporated in the final AI solutionmay be selected 11712. The component may include one or more of a model,an expert system, a neural network, and the like. After the componentfor the AI solution has been selected, configuration parameters may bedetermined 11714. The configuration parameters may be based, in part, onthe type of component selected, the brain region parameter, the activityparameter, the activity level, or the action parameter. Configuring andconfiguration parameters may include selecting an input for a machinelearning process, identifying an output to be provided by the machinelearning process, identifying an input for an operational solutionprocess 11716, identifying an output an operational solution process,tuning a learning parameter, identifying a change rates, identifying aweighting factor, identifying a parameter for inclusion, identifying aparameter for exclusion of a parameter, setting a threshold for inputdata, setting an output threshold for the operational robotic process,or setting a parameter threshold. Additionally, analysis of thefunctional media 11704 may include identifying a second brain regionparameter or a second activity parameter 11718. The component of the AIsolution may be revised 11720 based on the second brain region parameteror the second activity parameter. A second component of the AI solutionmay be selected 11722 based on the second brain region parameter or thesecond activity parameter. The final AI solution may be assembled fromthe component 11724 or the second component 11726. In embodiments, thefinal AI solution may be assembled from the component and the secondcomponents, optionally along with any standard or mandatory componentsthat enable operation.

Referring to FIG. 118, a computer-implemented method 11800 for selectingan AI solution for use in a robotic or automated process is depicted.The method may include receiving a user-related input 11802 comprising atimestamp and analyzing the user-related input 11804. The user-relatedinput may include an audio feed, a motion sensor, a video feed, aheartbeat monitor, an eye tracker, a biosensor (e.g. galvanic skinresponse), and the like. The analysis may enable the identification of aseries of user actions and associated activity parameters 11806. Acomponent for an AI solution may be selected based on a user action ofthe series of user actions 11808. The analysis may enable theidentification of a second user action of the series of user actions11810. Based on the second user action, the selected component for theAI solution may be revised 11812. A second component for the AI solutionmay be selected 11814 based on the second user action. An actionparameter may be identified 11816 based on the user action and/or theassociated activity parameters. For example, if the user action ismotion, an action parameter may include a range of motion, a speed ofmotion, a repetition of motion, a use of muscle memory, a smoothness ofmotion, a flow of motion, a timing of motion, and the like. The selectedcomponent of the AI solution may be configured 11818 based on the actionparameter. In embodiments, at least one device input performed by theuser may be received (11820). The device input may be synchronized withthe user actions based on the timestamp and a correlation between thedevice input and the user action determined 11819. The component may berevised 11823 based on the correlation. The selection of the componentof the AI solution may be partially based on the correlation between thedevice input and the user-related input 11821. The AI solution may beassembled 11822 from the component. The AI solution may be assembledfrom the second component 11824. In embodiments, the AI may be assembledfrom both the component and the second component, optionally along withany standard or mandatory components that enable operation.

Referring to FIG. 119, an illustrative and non-limiting example of anassembled AI solution 11902 is shown. The assembled AI solution 11902may include the selected component 11904 and a second selected component11906, as well as other components 11908. Configuration data 11914 forthe first selected component and configuration data 11912 for the secondselected component may be provided. Runtime input data 11910 may bespecified as part of the component configuration process. Components maybe structured to run serially (such as the selected component 11904 andthe second selected component 11906 which received input from theselected component 11904) or in parallel (such as the second component11906 and the other component(s) 11908). Some of the components mayprovide input for other components (such as the selected component 11904providing input to the second selected component 11906). Multiplecomponents may provide various portions of the overall AI solutionoutput 11918 (such as the second selected component 11906 and the othercomponents 11908). This depiction is not meant to be limiting and thefinal solution may include a varying number of components, configurationdata and input, as well as other components (e.g. sensors, voicemodulators, and the like) and may be interconnected in a variety ofconfigurations.

Referring to FIGS. 120-121, a computer-implemented method for selectingan AI solution for use in a robotic or automated process is depicted.The method may include receiving temporal biometric measurement data12002 of a worker performing a task and receiving spatial-temporalenvironmental data 12004 experienced by the worker performing the task.Using the received data, a spatial-temporal activity pattern may beidentified 12006. Based on the spatial-temporal activity pattern, anactive area of the worker's neocortex may be identified 12008. A type ofreasoning used when performing the task may be identified 12010 based onthe active area of the neocortex and/or the biometric measurement data,or the spatial-temporal environmental data. A component may be selected12012 for use in the AI solution to replicate the type of reasoning. Thecomponent of the AI solution may be configured 12014 based on thespatial-temporal environmental input. A determination may be made as towhether a serial or parallel AI solution is optimal 12016. A set ofconfiguration inputs to the component may be identified 12018 and anordered set of inputs to the component of the AI solution may beidentified 12020. Training the machine may include providing varioussubsets of the spatial-temporal environmental input to determineappropriate input weightings and identify efficiencies from combinationsof spatial-temporal environmental input 12022. Desirable or undesirablecombinations of the spatial-temporal environmental data may also beidentified 12024. Based on the identified required input, inputenvironmental data may be processed to reduce input noise 12026 (e.g.improve signal to noise for a signal of interest), filtered to providethe appropriate input signals to the component, and the like.

Continuing with reference to FIG. 121, a second temporal biometricmeasurement data of the same worker performing the task may be received12102 and a plurality of performed tasks identified from the biometricmeasurements 12104. A performance parameter may be extracted from thebiometric measurements 12106 (e.g. worker heartrate, galvanic skinresponse, and the like). In some embodiments, the component may beconfigured based on the performance parameter 12107. In someembodiments, the second temporal biometric measurements may be providedto the configuration module as a training set 12109. Results datarelated to the task may be received 12108 and the second temporalbiometric measurement data may be correlated with the received resultsdata 12110. In some embodiments, the component may be selected based, atleast in part, on the correlation 12111. A series of time intervalsbetween each of the plurality of performed tasks may be identified 12112and the component of the AI solution configured based on at least one ofthe time intervals 12114. For example, if the worker inspects an objectfor a long period of time before moving on to the next action, this mayindicate complex visual processing as well as mental processing and mayindicate that the corresponding component for the task be configured forin-depth, fine detail processing and the like.

Referring to FIG. 122, an AI solution selection and configuration system12202 is depicted. An example selection and configuration system 12202may include a media input module 12204 structured to receiveuser-related functional media 12214. The user-related functional media12214 may include images of a person engaged in a task to be automated,audio recordings, video feeds, biometric data (e.g. heartbeat data,galvanic skin response data, and the like), motion data, and the like. Amedia analysis module 12206 may analyze the received media and identifyan action parameter. The action parameter may be representative of atype of activity in which the person appears to be engaged such aswatching, listening, moving, thinking, and the like. In someembodiments, the functional media is indicative of a type of brainactivity of a human engaged in the task to be automated and the mediaanalysis module 122206 identifies an activity level in at least onebrain region and provide a brain region parameter corresponding with theactivity level in the identified brain region. The media analysis modulemay also identify an activity parameter indicative of a level ofengagement such as engaged, unengaged, level of activity, type ofactivity, and the like. A solution selection module 12208 may bestructured to select at least one component of the AI solution for usein the automated process based, at least in part, on the actionparameter, the brain region parameter, or the activity parameter. Thebrain region parameter or the action parameter may suggest a type ofcomponent to select and the activity parameter may suggest a level ofprocessing required for that component. For example, an action parameterof watching would suggest selecting a component suited to visualprocessing. If the activity parameter was representative of olfactoryprocession, the input specification module may identify at least onechemical sensor as an input. If the activity parameter is representativeof visual processing the input specification module 11216 may identifyat least one visual sensor as a robotic input. In some embodiments, thevisual sensor may be selected to be sensitive to a portion of thevisible spectrum with wavelengths between about 380 to 700 nanometers.If the activity parameter is representative of auditory processing, theinput specification module 11216 may identify at least one microphone asa robotic input. If the activity parameter was representative of a veryhigh level of concentration, the solution selection module 12208 maysuggest a level of processing that will be required, where theprocessing might occur, and the like. A component configuration module12210 may configure the component 12212. Configuring the component mayinclude: selecting an input for a machine learning process for theselected component, identifying an output to be provided by the machinelearning process, identifying an input for an operational solutionprocess, identifying an output an operational solution process, tuning alearning parameter, identifying a change rates, identifying a weightingfactor, identifying a parameter for inclusion, identifying a parameterfor exclusion of a parameter, setting a threshold for input data,setting an output threshold for the operational robotic process, settinga parameter threshold, and the like. A solution assembly module 12218may assemble the final AI solution based on one or more selectedcomponents, configuration components, and required runtime. An inputspecification module 12216 may suggest input sources based on theselected component, the action parameter, brain region parameter,activity parameter, or the like.

Referring to FIG. 123, an AI solution selection and configuration system12302 is depicted. An example selection system 12302 may include animage input module 12304 structured to receive functional images 12314of the brain such as, such as functional MM or other magnetic imaging,electroencephalogram (EEG), or other imaging, such as by identifyingbroad brain activity (e.g., wave bands of activity, such as delta,theta, alpha and gamma waves), by identifying a set of brain regionsthat are activated and/or inactive while the worker is performing one ofthe tasks to be automated. The image input module 12304 may provide asubset of the functional images 12314 to the image analysis module12306. In some embodiments the image input module 12304 may perform somepreprocessing for the subset of functional images 12314, such as noisereduction, histogram adjustment, filtering, and the like, prior toproviding the subset of functional images 12314 to the image analysismodule 12306. The image analysis module 12306, may identify an activitylevel in at least one brain region and provide a brain region parameterbased on the subset of functional images. The brain region parameter mayrepresent a specific region of the neocortex such as frontal, parietal,occipital, and temporal lobes of the neocortex, including primary visualcortex and the primary auditory cortex, or subdivisions of theneocortex, including ventrolateral prefrontal cortex (Broca's area), andorbitofrontal cortex. The brain region parameter may representfunctional areas of the brain, such as visual processing, inductivereasoning, audio processing, olfactory processing, muscle control, andthe like. A solution selection module 12308 may select a component foruse in an AI solution based on the brain region parameter, and provideinput into a component configuration module (such as selecting an inputfor a machine learning process, identifying an output to be provided bythe machine learning process, identifying an input for an operationalsolution process, identifying an output an operational solution process,tuning a learning parameter, identifying a change rates, identifying aweighting factor, identifying a parameter for inclusion, identifying aparameter for exclusion of a parameter, setting a threshold for inputdata, setting an output threshold for the operational robotic process,and setting a parameter threshold, and the like. The componentconfiguration module 12310, may use the input to configure the component12312. The solution selection module 12308 may also supply data to theinput specification module 12316. A solution assembly module 12318 maycombine the component, and other components, to create the AI solution.The AI solution may be set up to receive inputs as specified by theinput specification module 12316. Although one iteration of selecting acomponent is shown in this figure, it is envisioned, that multiplecomponents may be selected, configured and assembled as part of the AIsolution

Referring to FIGS. 124-125, an AI solution selection and configurationsystem 12402 is depicted. An example AI solution selection andconfiguration system 12402 may include an input module 12404 structuredto receive a variety of user-related input such as videos, audiorecording, heartbeat monitors, galvanic skin response data, motion data,and the like. There may be temporal data associated with theuser-related input. The input module 12404 may provide a subset of theuser-related input data 12414 to the input analysis module 12406. Theanalysis module 12406 may include a temporal analysis module 12418 toidentify timing of user-related actions. The temporal analysis module12418 may enable identification of timing of user actions. In someembodiments the input module 12404 may perform some preprocessing forthe subset of the user-related input data 12414, such as noisereduction, correlation between types of input data, and the like, priorto providing the subset of user-related input data 12414 to the inputanalysis module 12406. The input analysis module 12406, may identify atype of brain activity being engaged in (e.g. visual processing,auditory processing, olfactory processing, motion control, and the like)and a level of intensity of activity based on data such as heartbeatdata, galvanic skin response data and the like. A component selectionmodule 12408 may select a component for use in an AI solution based onthe type of brain activity and provide input into a componentconfiguration module 12410 which may include an ML input selectionmodule 12502 for selecting an input for a machine learning process, anMP output identification module 12504 for identifying an output to beprovided by the machine learning process, a runtime input selectionmodule 12506 for identifying an input for an operational solutionprocess, a runtime output identification module 12508 for identifying anoutput of the component, a settings module 12510 for identifying achange rate, identifying a weighting factor, setting a threshold forinput data, setting an output threshold for the operational roboticprocess, and the like, a parameter settings module 12512 for tuning alearning parameter, identifying a parameter for inclusion, identifying aparameter for exclusion, setting a parameter threshold, and the like.The component configuration module 12410 may configure the selectedcomponent 12412. The component selection module 12408 may also supplydata to the input specification module 12416. An AI solution assemblymodule 12420 may combine the configured component with other components,along with any standard or mandatory components, as necessary, to createthe AI solution. The AI solution may be set up to receive inputs asspecified by the input specification module 12416. Although oneiteration of selecting a component is shown in this figure, it isenvisioned, that multiple components may be selected, configured andassembled as part of the AI solution.

In embodiments, referring to FIG. 126, an AI solution selection andconfiguration system 12602 is depicted. An example AI solution selectionand configuration system 12602 may include a data input module 12604 toreceive an input stream including temporal user-related data 12614 whichmay include video streams, audio streams, equipment interactions (e.g.mouse clicks, mouse motion, physical input to a machine) user biometricssuch as heartbeat, galvanic skin response, eye tracking, and the like.The data input module 12604 may also receive temporal environmentalinput data 12620 representative of environmental input the user isreceiving such as a visual environment, an auditory environment,olfactory environment, equipment displays, a device user interface, andthe like. The data input module 12604 may also receive temporal resultsinput data 12603. The data input module 12604 may provide a subset ofthe received data 12614, 12620, 12603 to an input analysis module 12616.The data input module 12604 may process the received data 12614, 1262012603 to reduce noise, compress the data, correlate some of the data,and the like. The analysis module 12616 may identify a plurality of useractions to provide to the component selection module 12608. The imageanalysis module 12616 may include a temporal analysis module 12618 toidentify timing of user actions. The temporal analysis module 12618 mayallow for the correlation between temporal user-related data 12614,environmental data 12620, and results data 12603. Based on the useractions, the component selection module 12608 may select a componentthat would simulate one or more mental processes of the user needed toperform at least one of the plurality of user actions. Factors inidentifying the selected component may include the level ofcomputational intensity needed, time sensitivity, and the like. This maydictate a type of component, a location of component (on-board, in thecloud, edge-computing, and the like. The input analysis module 12616 mayalso provide information regarding the user's actions and environmentaldata to the component configuration module 12610. This data may be usedby the component configuration module as input to a machine learningalgorithm, in conjunction with the results data to identify which inputsare beneficial and which are detrimental to enabling the component toreach desired results, and identify appropriate weighting of inputs,parameter settings, and the like. The component configuration module12610 configures the component 12612 which is provided to the overall AIsolution 12624 together with configuration information.

As described elsewhere herein, this disclosure concerns systems andmethods for the discovery of opportunities for increased automation andintelligence, including solutions to domain-specific problems. Further,this disclosure also concerns selection and configuration of anartificial intelligence solution (e.g. neural networks, machine learningsystems, expert systems, etc.) once opportunities are discovered.

Referring now to FIG. 127, a controller 12708 includes an opportunitymining module 153, an artificial intelligence configuration module12704, and an artificial intelligence search engine 12710, optionallyhaving a collaborative filter 12728 and a clustering engine 12730. Theopportunity mining module 153 receives input 12702, such as attributeinput regarding an attribute of a task, a domain, or a domain-relatedproblem.

The input 12702 may be processed by the opportunity mining module 153 todetermine whether an artificial intelligence system can be applied tothe task or the domain. For example, the attribute input 12702 mayinclude an attribute of a task, domain or problem, such as a negotiatingtask, a drafting task, a data entry task, an email response task, a dataanalysis task, a document review task, an equipment operation task, aforecasting task, an NLP task, an image recognition task, a patternrecognition task, a motion detection task, a route optimization task,and the like. The opportunity mining module 153 may determine if one ormore attributes of the task are similar to other tasks that have beenautomated or to which an intelligence has been applied, or based on theattribute of the task, if the task is potentially automatable orsuitable to have an intelligence applied to it regardless of whether ithas been done previously. For example, attributes of a drafting task mayinclude articulating a first idea, articulating a second idea,articulating a plurality of ideas, combining the plurality of ideas in apairwise fashion, and combining the ideas in a triplicate fashion.Articulating ideas may not be suitable for automation, but the task ofcombining ideas pairwise or in triplicate form may be suitable forautomation or to have an intelligence applied to the task.

If a determination is made that an artificial intelligence system can beapplied to the task or the domain, the output 12712 regarding thatdetermination may be used to trigger an artificial intelligence searchengine 12710 to perform a search of an artificial intelligence store157. The artificial intelligence store 157 may include a plurality ofdomain-specific and general artificial intelligence models 12718, andcomponents of domain-specific and general artificial intelligence models12718. The artificial intelligence store 157 may be organized by acategory. The category may be at least one of an artificial intelligencemodel component type, a domain, an input type, a processing type, anoutput type, a computational requirement, a computational capability, acost, a training status, or an energy usage. The artificial intelligencestore may include at least one e-commerce feature. The at least onee-commerce feature may include at least one of a rating, a review, alink to relevant content, a mechanism for provisioning, a mechanism forlicensing, a mechanism for delivery, or a mechanism for payment. Models12718 may be pre-trained, or may be available for training. Componentsof domain-specific and general artificial intelligence models 12718 mayinclude artificial intelligence building blocks, such as a componentthat detects and translates between languages, or a component thatdelivers highly personalized customer recommendations. One or moremodels 12718 and/or components of a model 12718 may be identified in asearch of the artificial intelligence store 157. Components of a model12718 may be identified either as a stand-alone element to be used inthe assembly of a custom AI model 12718 or as a component of a complete,optionally pre-trained, model 12718.

The artificial intelligence store 157 may include metadata 12724 orother descriptive material indicating a suitability of an artificialintelligence system for at least one of solving a particular type ofproblem or operating on domain-specific inputs, data, or other entities.The metadata 12724, or other descriptive material, category, ore-commerce feature may be searched using the attribute input 12702and/or other selection criteria 12714. For example, attributes of a taskinvolving 2D object classification may be searched in the artificialintelligence store 157 and its metadata 12724 to reveal that anartificial intelligence model 12718 suitable for a task involving 2Dobject classification may be a convolutional neural network. Continuingwith the example, there may be model diversity even within the class ofconvolutional neural networks (CNN) in the artificial intelligence store157, such as a CNN calibrated to a certain type of 2D object recognition(e.g., straight edges) and another CNN calibrated to another kind of 2Dobject recognition (e.g., combo of curved and straight edges). In thisexample, if the further edge vs. curved attribute of the type of 2Dobject is searched, the artificial intelligence store 157 would presentthe CNN best suited to the 2D object to be classified.

In embodiments, in addition to the input 12702, at least one selectioncriteria 12714 may be used by the artificial intelligence search engine12710 to search the artificial intelligence store 157 for artificialintelligence models 12718 and/or components thereof. Selection criteriaused in the recommendation of an artificial intelligence model 12718 ormodel component may include at least one of if the model is pre-trainedor not, an availability of the at least one artificial intelligencemodel 12718 or component thereof to execute in a user environment, anavailability of the at least one artificial intelligence model 12718 orcomponent thereof to a user, a governance principle, a governancepolicy, a computational factor, a network factor, a data availability, atask-specific factor, a performance factor, a quality of service factor,a model deployment consideration, a security consideration, or a humaninterface, which may be elsewhere described herein. For example, agovernance principle, such as a requirement for an anti-bias review ofpedestrian accident-avoidance systems, may be used to search anartificial intelligence store 157 for artificial intelligence models toapply to an autonomous driving task. In another example, a selectioncriteria for an artificial intelligence solution to be used with airtraffic control system may be a requirement for having been trained onadversarial attacks and deceptive input. In yet another example, aselection criteria for an artificial intelligence solution to be usedwith an equities trading task may be the requirement for humanoversight, and particularly, human-based final decisions.

The artificial intelligence search engine 12710 may rank one or moreresults of the search according to a strength or a weakness of the atleast one artificial intelligence model 12718 or model componentrelative to the at least one selection criteria 12714. The ranked searchresults may be presented to a user for evaluation and consideration, andultimately, selection. In embodiments, the artificial intelligencesearch engine 12710 may further include a collaborative filter 12728that receives an indication of an element of the at least one artificialintelligence model 12718 or model component from a user that is used tofilter the search results. In embodiments, the artificial intelligencesearch engine 12710 may further include a clustering engine 12730structured to cluster search results comprising the at least oneartificial intelligence model 12718 or model component. The clusteringengine 12730 may be at least one of a similarity matrix or a k-meansclustering. The clustering engine 12730 may associate at least one ofsimilar developers, similar domain-specific problems, or similarartificial intelligence solutions in the search results.

Once an artificial intelligence model 12718 or components thereof areidentified by the artificial intelligence search engine 12710, either bysearching with the input 12702 alone or with both the input 12702 and aselection criteria 12714, an artificial intelligence configurationmodule 12704 may configure one or more data inputs 12720 to use with theat least one artificial intelligence model 12718 or model component. Theartificial intelligence configuration module 12704 may, in certainembodiments, be operative in discovering and selecting what inputs 12720may enable effective and efficient use of artificial intelligence for agiven problem. In embodiments, the artificial intelligence configurationmodule 12704 may further configure the at least one artificialintelligence model 12718 or model component(s) in accordance with atleast one configuration criteria 12722. In embodiments, individual datainputs and model components may be configured via one or moreconfiguration criteria, while in other embodiments, a singleconfiguration criteria governs configuration of data input, AI componentassembly, and the like.

In embodiments, the at least one configuration criteria 12722 mayinclude at least one of an availability of the at least one artificialintelligence model 12718 or model component to execute in a userenvironment, an availability of the at least one artificial intelligencemodel 12718 or model component to a user, a governance principle, agovernance policy, a computational factor, a network factor, a dataavailability, a task-specific factor, a performance factor, a quality ofservice factor, a model deployment consideration, a securityconsideration, or a human interface. In embodiments, the at least oneconfiguration criteria may include at least one of identifying a desiredoutput, identifying training data, identifying parameters for exclusionor inclusion in training or operation of the model, an input datathreshold, an output data threshold, a selection of a neural networktype, a selection of an input model type, a setting of initial modelweights, a setting of model size, a selection of computationaldeployment environment, a selection of input data sources for training,a selection of input data sources for operation, a selection of feedbackfunction/outcome measures, a selection of data integration language(s)for inputs and outputs, a configuration of APIs for model training, aconfiguration of APIs 11214 for model inputs, a configuration of APIs11214 for outputs, a configuration of access controls, a configurationof security parameters, a configuration of network protocols, aconfiguration of storage parameters, a configuration of economicfactors, a configuration of data flows, a configuration of highavailability, one or more fault tolerance environments, a price-baseddata acquisition strategy, a heuristic method, a decision to make adecision model, or a coordination of massively parallel decision makingenvironments. In embodiments, the at least one configuration criteriamay include parameters for assembly of an AI solution from a pluralityof identified model components, optionally along with other standard ormandatory model components. For example, the model components may beconfigured to run in parallel, to run serially, or in a combination ofserial and parallel.

For example, the artificial intelligence configuration module 12704 mayconfigure an artificial intelligence model 12718 to weight one datainput 12720 more heavily than another. For example, in the rain, anautonomous driving solution may weight input from a traction controlsystem and a forward radar system more heavily than sensors targeted toincreasing fuel efficiency, such as sensors measuring road slope andvehicle speed. After the rain, the weighting may be reversed.

In another example, the artificial intelligence configuration module12704 may configure an artificial intelligence model 12718 to operatewithin certain thresholds of data input 12720. For example, anartificial intelligence model 12718 may be used in a combinatorialdrafting task. When only two articulated ideas are provided to the model12718, the model 12718 may not be triggered to operate. However, oncethe model 12718 receives a third articulated idea, its combinatorialprocessing of articulated ideas may commence.

The artificial intelligence configuration module 12704 may configurewhich sensors to use as data input 12720, how frequently to sample data,how frequently to transmit output, the weighting of various data inputs12720, thresholds to apply to data from data inputs 12720, whether anoutput of one component of the model 12718 is used as input to anothercomponent of the model 12718, an order of operation of the components ofthe model 12718, a positioning of a model component within a workflow ofa model, and the like.

The artificial intelligence configuration module 12704 may configure anartificial intelligence model 12718 from one or more model componentsidentified by the artificial intelligence search engine 12710. Forexample, if the search result consisted solely of model components, theAI configuration module 12704 may configure where to place theidentified 127

components in relation to one another, such as in a workflow or dataflow, as well as in relation to other components that may be requiredfor the model 12718 to function.

In embodiments, an artificial intelligence store 157 may include a setof interfaces to artificial intelligence systems, such as enabling thedownload of relevant artificial intelligence applications, establishmentof links or other connections to artificial intelligence systems (suchas links to cloud-deployed artificial intelligence systems via APIs,ports, connectors, or other interfaces) and the like.

Referring now to FIG. 128, a method of artificial intelligence modelidentification and selection may include receiving input regarding anattribute of a task or a domain 12802, and processing the input todetermine whether an artificial intelligence system can be applied tothe task or the domain 12804, performing a search of an artificialintelligence store of a plurality of domain-specific and generalartificial intelligence models and model components using the inputand/or at least one selection criteria to identify at least oneartificial intelligence model or model component to apply to the task orthe domain 12808, and configuring one or more data inputs to use withthe at least one artificial intelligence model 12810 or model component.The artificial intelligence store may include metadata or otherdescriptive material indicating a suitability of an artificialintelligence system for at least one of solving a particular type ofproblem or operating on domain-specific inputs, data, or other entities.

The method may further include ranking one or more results of the searchaccording to a strength or a weakness of the at least one artificialintelligence model relative to the at least one selection criteria12812. The method may further include configuring the at least oneartificial intelligence model or model component in accordance with atleast one configuration criteria 12814. The method may further includecollaborative filtering search results comprising the at least oneartificial intelligence model using an element of the at least oneartificial intelligence model selected or model component by a user12816. The method may further include clustering search resultscomprising the at least one artificial intelligence model or modelcomponent with a clustering engine 12818.

In embodiments, one or more of the controllers, circuits, systems, datacollectors, storage systems, network elements, or the like as describedthroughout this disclosure may be embodied in or on an integratedcircuit, such as an analog, digital, or mixed signal circuit, such as amicroprocessor, a programmable logic controller, an application-specificintegrated circuit, a field programmable gate array, or other circuit,such as embodied on one or more chips disposed on one or more circuitboards, such as to provide in hardware (with potentially acceleratedspeed, energy performance, input-output performance, or the like) one ormore of the functions described herein. This may include setting upcircuits with up to billions of logic gates, flip-flops, multiplexers,and other circuits in a small space, facilitating high speed processing,low power dissipation, and reduced manufacturing cost compared withboard-level integration. In embodiments, a digital IC, typically amicroprocessor, digital signal processor, microcontroller, or the likemay use Boolean algebra to process digital signals to embody complexlogic, such as involved in the circuits, controllers, and other systemsdescribed herein. In embodiments, a data collector, an expert system, astorage system, or the like may be embodied as a digital integratedcircuit, such as a logic IC, memory chip, interface IC (e.g., a levelshifter, a serializer, a deserializer, and the like), a power managementIC and/or a programmable device; an analog integrated circuit, such as alinear IC, RF IC, or the like, or a mixed signal IC, such as a dataacquisition IC (including A/D converters, D/A converter, digitalpotentiometers) and/or a clock/timing IC.

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The present disclosure references one or more elements such ascontrollers, circuits, modules, engines, processors, or the like(“control elements”), which are structured to and/or configured toperform certain operations and/or procedures to illustrate embodimentsof the disclosure. A given control element may be described as a singledevice for clarity of the description, but a control element may be asingle device, or distributed across more than one device, where aspectsof the control element are embodied as all or part of the givendevice(s). Without limitation to any aspect of the present disclosure, acontrol element may be embodied as, and/or may be communicatively oroperatively coupled to, any one or more of: a sensor; an actuator; auser interface; a computing resource (e.g., a processor, a network,and/or a memory storage); and/or as executable instructions on acomputer readable medium.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable media, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherservers, clients, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs, or codes as describedherein and elsewhere may be executed by the server. In addition, otherdevices required for execution of methods as described in thisapplication may be considered as a part of the infrastructure associatedwith the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (SaaS), platformas a service (PaaS), and/or infrastructure as a service (IaaS).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, EVDO, mesh, or other network types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g. USB sticks or keys),floppy disks, magnetic tape, paper tape, punch cards, standalone RAMdisks, Zip drives, removable mass storage, off-line, and the like; othercomputer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general—purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosure,and does not pose a limitation on the scope of the disclosure unlessotherwise claimed. The term “set” may include a set with a singlemember. No language in the specification should be construed asindicating any non-claimed element as essential to the practice of thedisclosure.

While the foregoing written description enables one of ordinary skill tomake and use what is considered presently to be the best mode thereof,those of ordinary skill will understand and appreciate the existence ofvariations, combinations, and equivalents of the specific embodiment,method, and examples herein. The disclosure should therefore not belimited by the above described embodiment, method, and examples, but byall embodiments and methods within the scope and spirit of thedisclosure.

All documents referenced herein are hereby incorporated by reference asif fully set forth herein.

What is claimed is:
 1. A system for selection and configuration of anautomated robotic process, the system comprising: a media input modulestructured to receive at least one functional media; a media analysismodule structured to analyze the at least one functional media andidentify an action parameter; and a solution selection module structuredto select at least one component of an AI solution for use in anautomated robotic process, wherein the selection is based, at least inpart, on the action parameter.
 2. The system of claim 1, wherein: the atleast one functional media comprises a media indicative of brainactivity in a human engaged in a task of interest; and the mediaanalysis module is further structured to identify an activity level inat least one brain region and provide a brain region parametercorresponding with the activity level in the at least one brain region.3. The system of claim 2, wherein the solution selection module isfurther structured to select the at least one component of the AIsolution based, at least in part, on the brain region parameter.
 4. Thesystem of claim 2, wherein: the media analysis module is furtherstructured to provide an activity parameter that relates to the humanengaged task of interest and corresponds with the brain regionparameter; and the solution selection module is further structured toselect at least one component of the AI solution based, at least inpart, on the activity parameter.
 5. The system of claim 4, wherein theactivity parameter is selected from a list of activity parametersincluding at least one of engaged, unengaged, level of activity, or typeof activity.
 6. The system of claim 4, further comprising a componentconfiguration module structured to set a configuration parameter basedon, at least in part, at least one of the activity parameter or thebrain region parameter.
 7. The system of claim 2, wherein the solutionselection module is further structured to identify a runtime inputbased, at least in part, on the brain region parameter.
 8. The system ofclaim 2, wherein the brain region parameter is indicative of a neocortexregion including at least one of Fp1, F7, F3, T3, C3, T5, P3, O1, Fp2,F8, F4, T4, C4, T6, P4, or O2.
 9. The system of claim 2, wherein the atleast one selected component of the AI solution simulates a processingactivity similar to the activity of the brain region indicated by thebrain region parameter.
 10. The system of claim 4, wherein the activityparameter is representative of an activity including at least one ofolfactory processing, visual processing, auditory processing, or motion.11. The system of claim 10, wherein, when the activity parameter isrepresentative of the olfactory processing, an input specificationmodule is further structured to identify at least one chemical sensor asa robotic input.
 12. The system of claim 10, wherein, when the activityparameter is representative of the visual processing, an inputspecification module is further structured to identify at least onevisual sensor as a robotic input.
 13. The system of claim 12, wherein asensitivity of the at least one visual sensor comprises a portion of arange of wavelengths between about 380 to about 700 nanometers.
 14. Thesystem of claim 10, wherein, when the activity parameter isrepresentative of the auditory processing, an input specification moduleis further structured to identify at least one microphone as a roboticinput.
 15. The system of claim 2, wherein the media analysis module isfurther structured to identify a second brain region parameter.
 16. Thesystem of claim 15, wherein the second brain region parameter isindicative of at least one of: a resolution of the at least onefunctional media, a strength of an engagement signal, a relativestrength of an engagement signal between the brain region parameter andthe second brain region parameter, or an extent of a brain regionengagement.
 17. The system of claim 1, wherein the selected at least onecomponent of the AI solution includes at least one of a model, an expertsystem, a type of neural network, a specific machine-learning algorithm,a configuration specification, a specified input, a specified output, alearning parameter, a change rate, a weighting, or a threshold.
 18. Thesystem of claim 1, wherein: the at least one functional media comprisesa video feed of a human engaged in a task of interest; and the actionparameter is representative of an action including at least one oflistening, looking, smelling, or touching.
 19. The system of claim 1,wherein: the action parameter comprises an ordered series of actions;and the solution selection module is further structured to select aplurality of components for the AI solution, the selection is based, atleast in part, on the ordered series of actions.